Metodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidad

anexos, figuras, tablas.

Autores:
Coca Ortegón, Germán Augusto
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2021
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/79474
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/79474
https://repositorio.unal.edu.co/
Palabra clave:
670 - Manufactura
industria metalmecánica
sector industrial
metalworking industry
industrial sector
algoritmos genéticos multiobjetivo
configuración de “taller de trabajo flexible”
sector metalmecánico
emisiones dióxido de carbono
postura
tardanza total
Sustainability
multi-objective genetic algorithms
“Flexible Job shop” configuration
metal mechanic sector
carbon dioxide emissions
posture
total tardiness
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id UNACIONAL2_6fe46740d5a903c0ea80de041456a472
oai_identifier_str oai:repositorio.unal.edu.co:unal/79474
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Metodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidad
dc.title.translated.eng.fl_str_mv Multiobjective methodology for "flexible job shop" manufacturing systems production scheduling of metalmechanic sector, under a sustainability approach
title Metodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidad
spellingShingle Metodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidad
670 - Manufactura
industria metalmecánica
sector industrial
metalworking industry
industrial sector
algoritmos genéticos multiobjetivo
configuración de “taller de trabajo flexible”
sector metalmecánico
emisiones dióxido de carbono
postura
tardanza total
Sustainability
multi-objective genetic algorithms
“Flexible Job shop” configuration
metal mechanic sector
carbon dioxide emissions
posture
total tardiness
title_short Metodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidad
title_full Metodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidad
title_fullStr Metodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidad
title_full_unstemmed Metodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidad
title_sort Metodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidad
dc.creator.fl_str_mv Coca Ortegón, Germán Augusto
dc.contributor.advisor.none.fl_str_mv Castrillón Gómez, Omar Danilo
Ruiz Herrera, Santiago
dc.contributor.author.none.fl_str_mv Coca Ortegón, Germán Augusto
dc.subject.ddc.spa.fl_str_mv 670 - Manufactura
topic 670 - Manufactura
industria metalmecánica
sector industrial
metalworking industry
industrial sector
algoritmos genéticos multiobjetivo
configuración de “taller de trabajo flexible”
sector metalmecánico
emisiones dióxido de carbono
postura
tardanza total
Sustainability
multi-objective genetic algorithms
“Flexible Job shop” configuration
metal mechanic sector
carbon dioxide emissions
posture
total tardiness
dc.subject.ocde.none.fl_str_mv industria metalmecánica
sector industrial
metalworking industry
industrial sector
dc.subject.proposal.spa.fl_str_mv algoritmos genéticos multiobjetivo
configuración de “taller de trabajo flexible”
sector metalmecánico
emisiones dióxido de carbono
postura
tardanza total
dc.subject.proposal.eng.fl_str_mv Sustainability
multi-objective genetic algorithms
“Flexible Job shop” configuration
metal mechanic sector
carbon dioxide emissions
posture
total tardiness
description anexos, figuras, tablas.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-05-04T19:37:53Z
dc.date.available.none.fl_str_mv 2021-05-04T19:37:53Z
dc.date.issued.none.fl_str_mv 2021-03-12
dc.type.spa.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TD
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/79474
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Nacional
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/79474
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Universidad Nacional
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Abdelmaguid, T. (2015). A neighborhood search function for flexible job shop scheduling with separable sequence-dependent setup times. Applied Mathematics and Computation, 260, 188–203. Recuperado de http://doi.org/10.1016/j.amc.2015.03.059.
Adam,S.,& Bruno, R. (2015). ReviewPareto’s 80/20 law and social differentiation: A social entropy perspective. Public Relations Review, 41, 178–186. Recuperado de http://dx.doi.org/10.1016/j.pubrev.2014.11.006.
Ahmad,S., Wong,K., Tseng,M., & Wong,W. (2018). Sustainable product design and development: A review of tools, applications and research prospects. Resources, Conservation & Recycling, 132, 49–61.
Ahmadi, E., Zandieh, M., Farrokh, M., & Emami, S. (2016). A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms. Computers and Operations Research, 73, 56-66. doi:10.1016/j.cor.2016.03.009.
Ahmed,W., Ashraf,M., Khan,S., Sarpong,S., Arhin,F., Sarpong., & Najmi,A. (2020). Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm’s sustainable performance. Operations Management Research , 13, 4–21.
Akbar,M., & Irohara,T. (2018). Scheduling for sustainable manufacturing: A review. Journal of Cleaner Production 205 (2018) 866e883, 205, 866- 883. Recuperado de https://doi.org/10.1016/j.jclepro.2018.09.100.
Adewuyi, O.B., Shigenobu, R., Senjyu, T., Lotfy, M.E., & Howlader, A.M. (2019). Multiobjective mix generation planning considering utility-scale solar PV system and voltage stability: Nigerian case study. Electr. Power Syst. Res. 168, 269–282. https://doi.org/10.1016/j.epsr.2018.12.010
Akgun, I.,& Erdal,H. (2019). Solving an ammunition distribution network design problem using multi-objective mathematical modeling, combined AHP-TOPSIS, and GIS. Computers & Industrial Engineering, 129, 512-528. Obtenido de https://doi.org/10.1016/j.cie.2019.02.004.
Allahverdi, A. (2015). The third comprehensive survey on scheduling problems with setup times/costs. . European Journal of Operational Research, 246(2), 345-378.
Allahverdi, A., Ng, C. T., Cheng, T. E., & Kovalyov, M. Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, 187(3), 985-1032.
Almahmoud,E., & Doloi, H. (2015). Assessment of social sustainability in construction projects using social network analysis. Facilities, 33, 152-176. Recuperado de https://doi.org/10.1108/F-05-2013-0042.
Azzouz, A., Chaabani, A., Ennigrou, M., & Said, L. (2020). Handling Sequence-dependent Setup Time Flexible Job Shop Problem with Learning and Deterioration Considerations using Evolutionary Bi-level Optimization. Applied Artificial Intelligence, 34:6, 433-455, doi: 10.1080/08839514.2020.1723871.
Amin,N., Noah,R.,Quek, J., Oxley ,J., & Rusli,B. (2020). Perceived physical demands in relation to work-related musculoskeletal disorders among nurses. Materials Today: Proceedings , 7(21),1-6. Recuperado de https://doi.org/10.1016/j.matpr.2020.01.196.
Amiri, F., Shirazi, B., & Tajdin, A. (2019). Multi-objective simulation optimization for uncertain resource assignment and job sequence in automated flexible job shop. Applied Soft Computing Journal 75 (2019) 190–202, 75, 190–202. Recuperado de https://doi.org/10.1016/j.asoc.2018.11.015.
Amini, M., & Bienstock, C. (2014). Corporate sustainability: an integrative definition and framework to evaluate corporate practice and guide academic research. Journal of Cleaner Production, 76, 12–19. Recuperado de http://doi.org/10.1016/j.jclepro.2014.02.016.
An, Y., Chen, X., Zhang, J., & Li,L. (2020). A hybrid multi-objective evolutionary algorithm to integrate optimization of the production scheduling and imperfect cutting tool maintenance considering total energy consumption. Journal of Cleaner Production, 1-28. Recuperado de https://doi.org/10.1016/j.jclepro.2020.121540.
Ardjmand,E., Sanei,O., & Youssef,O. (2019). Using list-based simulated annealing and genetic algorithm for order batching and picker routing in put wall based picking systems. Applied Soft Computing Journal , 75, 106–119. Recuperado de https://doi.org/10.1016/j.asoc.2018.11.019.
Ari,E., Karatepe,O., Rezapouraghdam,H., & Avci,V. (2020). A Conceptual Model for Green Human Resource Management: Indicators, Diferential Pathways,and Multiple Pro-Environmental Outcomes. Sustainability doi:10.3390/su12177089 www.mdpi, 12, 1-18. doi:10.3390/su12177089 www.mdpi.
Arcidiacono, G., Costantino, N., Yang, K., 2016. The AMSE lean six sigma governance model. Int. J. Lean Six Sigma 7, 233e266. https://doi.org/10.1108/IJLSS-06-2015- 0026
Armindo,J., Fonseca,J., Abreu,I & Toldy,T. (2019). Perceived importance of sustainability dimensions in the Portuguese metal industry. International Journal of Sustainable Development & World Ecology, 26:2, 154-165. doi: 10.1080/13504509.2018.1508524.
Aryanny,E.,& Iriani. (2019). Analysis of Quality Management by Implementing Total Quality Management Based on Deming Prize. International Conference on Science and Technology , 2-7. doi:10.1088/1742-6596/1569/3/032015.
Aschauer,A., Roetzer.,A., Steinboeck,A., & Kugi. (2017). An Efficient Algorithm for Scheduling a Flexible Job Shop with Blocking and No-Wait Constraints. IFAC PapersOnLine, 50 (1), 12490–12495.
Autuori,J., Hnaien,F.,& Yalaoui. (2016). Three metaheuristics improved by a mapping method. IFAC (International Federation of Automatic Control), 49-12, 1472–1477. doi: 10.1016/j.ifacol.2016.07.779.
Bahar, N., Noor, Z., Aris, A., & Kamaruzaman, N. (2020). An indicator framework approach on manufacturing water assessment towards sustainable water demand management. Journal of Environmental Treatment Techniques, 8(3), 875-883.
Balaraju, G., Venkatesh, S., & Reddy, B. (2014). Multi-objective flexible job shop scheduling using hybrid differential evolution algorithm. International Journal of internet Manufacturing and Services, 3(3), 226-243.
Bamgbade,J., Kamaruddeen,M., & Nawi,M. (2017). Malaysian construction firms social sustainability via organizational innovativeness and government support: The mediating role of market culture. Journal of Cleaner Production, 154, 114-124. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2017.03.187.
Banco Mundial. (2016). Datos sobre las cuentas nacionales del Banco Mundial. Obtenido de http://datos.bancomundial.org/indicador/NY.GDP.PCAP.CD
Banerjee,S.,& . (2019). The occupational illness of slum dwellers across industries: A case study in West Bengal. International Journal of Industrial Ergonomics , 73 (10), 1-9. Recuperado de https://doi.org/10.1016/j.ergon.2019.102834.
Belhadi,A.,Sha’ri,Y., Touriki,F & El Fezazi,S. (2018). Lean production in SMEs: literature review and reflection on future challenges. Journal of Industrial and Production Engineering, 35 (6), 368–382.
Bihari M., & Kane P. (2021). Evaluation and Improvement of Makespan Time of Flexible Job Shop Problem Using Various Dispatching Rules—A Case Study. In: Kalamkar V., Monkova K. (eds). Mechanical Engineering, 11 (9), 21-31. Recuperado de https://doi-org.ezproxy.unal.edu.co/10.1007/978-981-15-3639-7_73.
Bi,X.,& Wang,C. (2018). A niche-elimination operation based NSGA-III algorithm for many-objective optimization. Appl Intell, 48,118–141. doi:10.1007/s10489-017-0958-4.
Bissoli, D.,Altoe,W., Mauri,G & Amaral,A. (2018). A simulated annealing metaheuristic for the bi-objective flexible job shop scheduling problem,. International Conference on Research in Intelligent and Computing in Engineering (RICE), San Salvador, 1-6, doi: 10.1109/RICE.2018.8627907.
Bissoli,D., Zufferey,N., & Amaral,A. (2019). Lexicographic optimization-based clustering search metaheuristic for the multiobjective flexible job shop scheduling problem. International Transactions in Operational Research, 1–26.
Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 41 (3), 157-183. Recuperado dehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-34250084648&doi=10.1007%2fBF02023073&partnerID=40&md5=e855d121de089837fab8733d864abc60.
Brit, J. (2015). Stochastic Goal Programming and a Metaheuristic for Scheduling of Operating Rooms. Canadá: Universidad de Windsor .
Bukchin, Y., Hanany, E., 2020. Decentralization cost in two-machine job-shop scheduling with minimum flow-time objective. IISE Trans. 52(12), 1386–1402. https://doi.org/10.1080/24725854.2020.1730528
Burki,u.,Ersoy,p., & Dahlstrom,B. (2018). Achieving triple bottom line performance in manufacturer-customer supply chains: Evidence from an emerging economy. Journal of Cleaner Production , 197, 1307-1316. Recuperado de https://doi.org/10.1016/j.jclepro.2018.06.236.
Büyüközkan, B., Kayakutlu,G.,& Karakadılar,I. (2015). Assessment of lean manufacturing effect on business performance using Assessment of lean manufacturing effect on business performance using. Expert Systems with Applications, 42, 6539–6551. Recuperado de http://dx.doi.org/10.1016/j.eswa.2015.04.016.
Cai,Y., Wang,F., Tan,G., Hu,Z., Wang,Y., Lun,W., Wu,W., Liu,Y & Zhou,R. (2019). Hybridization of Bornean Melastoma: implications for conservation of endemic plants in Southeast Asia. Botany Letters, 166 (2), 117–124. Recuperado de https://doi.org/10.1080/23818107.2019.1585284.
Caldeira R.H., & Gnanavelbabu A. (2021). Solving the Flexible Job Shop Scheduling Problem Using a Hybrid Artificial Bee Colony Algorithm. En S. N. Vijayan S., Trends in Manufacturing and Engineering Management. Springer.
Caldwell,J., Caldwell,L., Thompson,L., & Lieberman,H. (2019). Fatigue and its management in the workplace. Neuroscience and Biobehavioral Reviews, 96, 272–289.
Calleja, G., & Pastor, R. (2013). A dispatching algorithm for flexible job-shop scheduling with transfer batches: an industrial application. Production Planning & Control. Obtenido de http://www.tandfonline.com/doi/abs/10.1080/09537287.2013.782846
Canales,A., & Campos,A. (2016). Modelamiento Predictivo de la Pérdida Auditiva Laboral, Relacionada con el Tratamiento de Absorción Acústica en una Industria Metal-Mecánica en Chile. Ciencia & trabajo, 11(9), 21-29.
Carpio de los Pinos,A., & González,M . (2020). Development of the Protocol of the Occupational Risk Assessment Method for Construction Works: Level of Preventive Action. International Journal of Environmental Research and Public Health, 17, 6369, 1-33. doi:10.3390/ijerph17176369.
Castro, V., Herrera, R., & Villalobos, M. (2020). Development of a web software to generate management plans of software risks. Revista Información Tecnológica, 31(3), 135-148. Recuperado de http://dx.doi.org/10.4067/S0718-07642020000300135.
CCMPC. (2019). Cámara de Comercio de Manizales por Caldas. Informe económico anual - Manizales y Caldas. Manizales.
CCMPC. (2014). Camara de Comercio de Manizales por Caldas.Caracterización sector metalmecánico de la ciudad de Manizales. Manizales.
CCS. (2019). Consejo Colombiano de Seguridad. Cómo le fue a Colombia en accidentalidad, enfermedad y muerte laboral en 2018. Obtenido de https://ccs.org.co/como-le-fue-a-colombia-en-accidentalidad-enfermedad-y-muerte-laboral-en-2018/
CESEDEN. (2018). Centro Superior de la Defensa Nacional. La Inteligencia artificial aplicada a la defensa.
Charnes,A., Cooper,W.,& Ferguson,R. (1995). Optimal Estimation of Executive Compensation by Linear Programming. Management Science, 138-151, Recuperado de https://doi.org/10.1287/mnsc.1.2.138.
Charniak,E & McDermott. (1985). Introduction to Artificial Intelligence. Reading, Mass. Addison-Wesley.
Chen, H., Jiang, Z., Zuo, L., & Zhang, Y. (2015). Multi-objective flexible job-shop scheduling problem based on NSGA-Ⅱ with close relative variation. Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, 46(4), 344–350. Recuperado de http://doi.org/10.6041/j.issn.1000-1298.2015.04.051.
Chen, Y., Wang, J., & Yang, Z. (2015a). The human factors/ergonomics studies for respirators: a review and future work. International Journal of Clothing Science and Technology, 27(5), 652–676. Recuperado de http://doi.org/10.1108/IJCST-06-2014-0077.
Chen,G., Johnson,K., Morales,E., Ibáñez,A., & Lin,T. (2018). A high-Oil castor cultivar developed through recurrent selection. Industrial Crops & Products, 111, 8–10. Recuperado de http://dx.doi.org/10.1016/j.indcrop.2017.09.064.
Chen, X., Liang, Y., Sterna, M., Wang, W., & Błażewicz, J. (2020a). Fully polynomial time approximation scheme to maximize early work on parallel machines with common due date. Eur. J. Oper. Res. 284, 67–74. https://doi.org/10.1016/j.ejor.2019.12.003
Chen,X., An,Y., Zhang,Z., & Li,Y. (2020). An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals. Journal of Manufacturing Systems , 54, 227–241.
Cheng,C., , Zhijun,C., & Chenghua,S. (2003). A novel design method: a genetic algorithm applied to an erbium-doped fiber amplifier. Optics Communications, 227 (11), 371–382.doi:10.1016/j.optcom.2003.09.055.
Chetan., Ghosh, S., & Venkateswara Rao, P. (2015). Application of sustainable techniques in metal cutting for enhanced machinability: a review. Journal of Cleaner Production, 100, 17–34. Recuperado de http://doi.org/10.1016/j.jclepro.2015.03.039.
Chiang, T., & Lin, H. (2011). Flexible job shop scheduling using a multiobjective memetic algorithm. . Proceedings of the 7th International Conference on Intelligent Computing (ICIC). Zhengzhou, China, 49-56. Recuperado de http://link.springer.com/chapter/10.1007.
Chiang, T., & Lin, H. (2013). A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. International Journal of Production Economics, 141(1), 87–98. Recuperado de http://doi.org/10.1016/j.ijpe.2012.03.034.
Chunguang,B., Sarpong,S.,& Sarkis,J. (2017). An implementation path for green information technology systems in the Ghanaian mining industry. Journal of Cleaner Production, 164, 1105-1123.
Coca, G. A., Castrillón, O. D., & Ruiz, S. (2015a). Design of a Novel Methodology for Multiobjective Anlysis in a Job Shop Manufacturing System, Based on Comparison of Subpopulations. In Proceedings of the 27th European Conference on Operational Research. July 12-15, 2015. Glasgow, Scotland: University of Strathclyde, 224. Recuperado de https://www.euro- online.org/media_site/reports/EURO27_AB.pdf.
Coca, G., Castrillón, O., & Ruiz, S. (2015b). El sonido y la iluminación en la programación multiobjetivo de un sistema de manufactura tipo “Job Shop”. Memorias del décimo primer congreso de Investigación Operativa – ÓPTIMA 2015. Octubre 18 al 21 de 2015. Ediciones Universitarias : Antofagasta - Chile.
Coca, G. A., Castrillón, O. D., & Ruiz, S. (2016). Programación de un Sistema de Fabricación tipo “job shop” bajo un Enfoque de Sostenibilidad. Revista Información Tecnológica, 27(6), 31-52. doi: 10.4067/S0718-07642016000600005.
Coca, G. A., Castrillón, O. D., & Ruiz, S. (2018). Los grupos de interés en la programación de producción de sistemas de manufactura “Job Shop” utilizando un algoritmo genético multiobjetivo. Proceeding of the XIX Latin Iberoamerican Conference on Operations Research - CLAIO 2018. September 24-27, 2018. Lima, Perú: @ Sociedad Peruana de Investigación Operativa (SOPIOS), 55, Recuperado de http://www.sopios.org.pe/static/claio/proceeding.pdf.
Coca, G. A., Castrillón, O. D., & Ruiz, S. (2019a). Los grupos de interés en la programación de producción de un sistema de manufactura “Job Shop” . Revista EIA, 16(32), 65-84. Recuperado de https://doi.org/10.24050/reia.v16i32.1236.
Coca, G. A., Castrillón, O. D., Ruiz, S., Mateo-Sanz, J., & Jiménez, L. (2019b). Sustainable evaluation of environmental and occupational risks scheduling flexible job shop manufacturing systems. Journal of Cleaner Production, 209, 146-168. Recuperado de https://doi.org/10.1016/j.jclepro.2018.10.193.
Coello, A., Christiansen & Aguirre,H. (1995). Multiobjective design optimization of counterweight balancing of a robot arm using genetic algorithms. Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence. Herndon, VA, USA , 20-23.doi: 10.1109/TAI.1995.479374.
Comisión Europea. (2016). Acuerdo de París. Obtenido de https://ec.europa.eu/clima/policies/international/negotiations/paris_es
Confecámaras. (2017). Contribución de las iniciativas clúster al desarrollo regional. Bogotá.
Conpes 3918. (2018). Estrategia para la implementación de los objetivos de desarrollo sostenible (ods) en Colombia. Bogotá: Consejo Nacional de Política Económica Y Social.Departamento Nacional De Planeación.
Cooper,M. (2019). The efficacy of industrial safety science constructs for addressing serious injuries & fatalities (SIFs). Safety Science , 120, 164–178. Recuperado de https://doi.org/10.1016/j.ssci.2019.06.038.
Corentin, L. (2019). Integrating waste minimization concerns in operations scheduling. These de Doctorat de L’Universite de Lyon (Ecole Doctorale Informatique et Mathématiques de Lyon). Recuperado de : http://theses.insa-lyon.fr/publication/2019LYSEI111/these.pdf.
Cuayal,J.,& Romero,L. (2016). Documentación del sistema de gestión ambiental en el marco de la NTC-ISO 14001 para la E.S.E hospital universitario San Jorge de la ciudad de Pereira. Pereira, Risaralda: Universidad Tecnológica de Pereira.
Cui,Z., Chang,Y., Zhang,J., Cai,X., & Zhang,W. (2019). Improved NSGA-III with selection-and-elimination operator. Swarm and Evolutionary Computation , 49, 23–33. Recuperado de https://doi.org/10.1016/j.swevo.2019.05.011.
D´Atlas. (2018). Manizales Met. Obtenido de http://datlascolombia.bancoldex.com/#/location/49?locale=es-col
D´Atlas. (2018). Medellín Met. Obtenido de http://datlascolombia.bancoldex.com/#/location/34
Dai,M., Tang,D., Giret,A., Salido.M. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer Integrated Manufacturing, 59, 143–157. Recuperado de https://doi.org/10.1016/j.rcim.2019.04.006.
DANE. (2020). Departamento Administrativo Nacional de Estadística. Encuesta mensual manufacturera con enfoque territorial (EMMET) Históricos. Obtenido de https://www.dane.gov.co/index.php/estadisticas-por-tema/industria/encuesta-mensual-manufacturera-con-enfoque-territorial-emmet/emmet-historicos
DANE. (2020). Departamento Administrativo Nacional de Estadística. Producto Interno Bruto (PIB) Base 2015. Obtenido de https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-trimestrales
Darmawan,M., Widhiarti,R., & Teniwut, Y. (2018). Green productivity improvement and sustainability assessment of the motorcycle tire production process: A case study. Journal of Cleaner Production , 191, 273- 282. Recuperado de https://doi.org/10.1016/j.jclepro.2018.04.228.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms . London: Wiley & Sons.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2), 182-197.
Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601. Recuperado de http://doi.org/10.1109/TEVC.2013.2281535.
Deliktas, D., Torkul,O., & Ustun,O. (2019). A flexible job shop cell scheduling with sequence-dependent family setup times and intercellular transportation times using conic scalarization method. International Transactions in Operational Research, 26, 2410–2431.
Demir, Y., & İşleyen, S. (2014). An effective genetic algorithm for flexible job-shop scheduling with overlapping in operations. International Journal of Production Research, 52(13), 3905-3921.
Dendena, B., & Corsi, S. (2015). The Environmental and Social Impact Assessment (ESIA): a further step towards an integrated assessment process. Journal of Cleaner Production, 108, 965–977. Recuperado de http://doi.org/10.1016/j.jclepro.2015.07.110.
Deng,Q., Gong,G., Gong,X., Zhang,L.,Liu,X., & Ren,Q. (2017). A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling. Computational Intelligence and Neuroscience, 1-21. Recuperado de https://doi.org/10.1155/2017/5232518.
DEusanio,M ., Zamagni,A., & Petti,L. (2019). Social sustainability and supply chain management: Methods and. Journal of Cleaner Production , 235, 178- 189. Recuperado de https://doi.org/10.1016/j.jclepro.2019.06.323.
Dietz,J., & Mulder, H. (2020). A History of Organisation and ICT. En Enterprise Ontology. The Enterprise Engineering Series. Springer, Cham.
Digalwar,A., Dambhare,S.,& Saraswat,S. (2020). Social sustainability assessment framework for indian manufacturing industry. Materials Today: Proceedings, 8(11), 1-8- Recuperado de https://doi.org/10.1016/j.matpr.2019.12.226.
DNP. (2009). Departamento Nacional de Planeación. Metalmecánica. Obtenido de https://colaboracion.dnp.gov.co/CDT/Desarrollo%20Empresarial/metalmecanica.pd
DNP. (2018). Departamento Nacional de Planeación. Política para el mejoramiento de la calidad del aire. Bogotá.
DNP. (2017). Departamento Nacional de Planeación. Diagnóstico de Crecimiento Verde. Análisis macroeconómico y evaluación del potencial de crecimiento verde. Bogotá. Domínguez Machuca, J. A., Álvarez Gil, M. J., Domínguez Machuca, M. A., González, S. G., & Ruiz Jiménez, A. (1995). Dirección de operaciones-Aspectos estratégicos en la producción y los servicios. McGraw Hill, España.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26(1), 29-41.
Dóro, L., Pandolfo.,A., & Panosso,R . (2020). Developing a specific structured procedure to assess sustainability performance in manufacturing processes. Journal of Cleaner Production, 7(11), 1- 40. Recuperado de https://doi.org/10.1016/j.jclepro.2020.122404.
Drechsel,D., Barlow,C., Bare,J., Jacobs,N.,& Henshaw,J. (2018). Historical evolution of regulatory standards for occupational and consumer exposures to industrial talc. Regulatory Toxicology and Pharmacology, 92, 251–267. Recuperado de https://doi.org/10.1016/j.yrtph.2017.12.005.
Earnhart, D., & Leonard, J. (2016). Environmental audits and signaling: The role of firm organizational structure. Resource and Energy Economics, 44(16), 1-22.
Ebrahimi,A., Jeon,H., & Leeb, C. (2020). Minimizing total energy cost and tardiness penalty for a scheduling-layout problem in a flexible job shop system: A comparison of four metaheuristic algorithms. Computers & Industrial Engineering , 141, 1-21. Recuperado https://doi.org/10.1016/j.cie.2020.106295.
Elkington, J. (1994). Towards the sustainable corporation: win-win-win business strategies for sustainable development. Calif. Manag. Rev, 36, 90 - 100.
Elkington, J. (1998). Partnerships from Cannibals with Forks: the triple bottom line of 21st-century business. Environ. Qual. Manag, 8(11), 37- 51.
EPA. (2017). Agencia de Proteccion Ambiental de los Estados Unidos. Descripción general de los gases de efecto invernadero. Obtenido de https://www.epa.gov/ghgemissions/overview-greenhouse-gases
Ervurala, B., Zaima,S., Demireld, O., Aydinb,Z., & Dursun,D. (2018). An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey’s energy planning. Renewable and Sustainable Energy Reviews, 82, 1538–1550.
FACTS. (2015). Agencia Europea para la Seguridad y la Salud en el Trabajo. Los efectos del ruido en el trabajo. España.
Fazel Zarandi, M.H., Sadat Asl, A.A., Sotudian, S., Castillo, O., 2020. A state of the art review of intelligent scheduling, Artificial Intelligence Review. Springer Netherlands, 53, 501-593. https://doi.org/10.1007/s10462-018-9667-6
Fadil,A., Chetouane,F & Binder,Z. (1997). Control of flexible job-shop: disturbances and robustness. IFAC Management and Control of Production and Logistics, 13-18.
Famiyeh, S., Kwarteng, A., Asante-Darko, D., y Dadzie, S. A. (2018). Green supply chain management initiatives and operational competitive performance. Benchmarking, 25(2), 607–631. https://doi.org/10.1108/BIJ-10-2016-0165
Fasecolda. (2020). Reporte por clase de riesgo y actividad económica - Consolidado. Obtenido de https://sistemas.fasecolda.com/rldatos/Reportes/xClaseGrupoActividad.aspx
Ferreira,V., Pinto,O., Arroyo,R., & Castro,E. (2018). Berry color variation in grapevine as a source of diversity. Plant Physiology and Biochemistry, 132, 696–707. Recuperado de https://doi.org/10.1016/j.plaphy.2018.08.021.
Fitzpatrick, J. (2016). Environmental sustainability assessment of using forest wood for heat energy in Ireland. Renewable and Sustainable Energy Reviews, 57, 1287–1295. Recuperado de http://doi.org/10.1016/j.rser.2015.12.197.
Fleming,J. (1985). Computer aided design of regulators using multiobjective optimization. Annual Review in Automatic Programming, 13(2), 47-52. Recuperado de https://doi.org/10.1016/0066-4138(85)90462-8.
Gao, J., Gen, M., & Sun, L. (2007). Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm. Journal of Intelligent Manufacturing, 17(4), 493–507. Recuperado de http://doi.org/10.1007/s10845-005-0021-x.
Gao, K., Suganthan, P., Pan, Q., Chua, T., Cai, T., & Chong, C. (2014). Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling. Information Sciences, 289(67), 76–90. Recuperado de http://doi.org/10.1016/j.ins.2014.07.039.
Gao, K., Suganthan, P., Pan, Q., Chua, T., Chong, C., & Cai, T. (2016a). An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Systems with Applications, 65, 52-67. doi: 10.1016/j.eswa.2016.07.046.
Gao, L., Zhou, B., Yang, L., & Wang, J. (2015). A multi-objective integrated optimization method for fjsp based on multi-rule resource allocation. Journal of Shanghai jiaotong University, 49(08), 1191–1198. Recuperado de http://xuebao.sjtu.edu.cn/CN/abstract/abstract10626.shtml.
Gao,J., Gen,M., Sun,L., & Zao,X. (2007a). A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computers & Industrial Engineering, 53, 149–162.
Gao,K., Suganthan,P., Chua,T., Chong,C., Cai,T., & Pan, Q. (2015a). A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert Systems with Applications , 42, 7652–7663.
Gao,K., Suganthan,P., Pan,Q., Tasgetiren,M., & Sadollah,A. (2016b). Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion. Knowle dge-Base d Systems, 109, 1–16.
Gao,K., Suganthan,P., Tasgetiren,M., Pan,Q., & Sun,Q. (2015b). Effective ensembles of heuristics for scheduling flexible job shop problem with new job insertion. Computers & Industrial Engineering , 90, 107–117.
Gao,Z., Sun,D., Zhao, R., & Dong, Y. (2021). Ship-unloading scheduling optimization for a steel plant. Information Sciences, 544, 214–226.
Gao,L.,& Pan,Q. (2016). A shuffled multi-swarm micro-migrating birds optimizer for a multi-resource-constrained flexible job shop scheduling problem. Information Sciences , 372, 655–676.
Garcia,S., Cintra,Y., Torres,C ., Guasti,F. (2016). Corporate sustainability management: a proposed multi-criteria model to support balanced decision-making. Journal of Cleaner Production, 136 (2016) 181-196. Recuperado de http://dx.doi.org/10.1016/j.cor.2016.03.009.
Geoffrion, A., Dyer, J., & Feinberg, A. (1972). An interactive approach for multi-criterion optimization, with an application to the operation of an academic department. Management Science, 19(4), 357-368. Recuperado de https://doi.org/10.1287/mnsc.19.4.357.
Geyik,F., & Dosdog˘ru,T. (2013). Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach. Neural Comput & Applic, 23, 1631–1641.doi: 10.1007/s00521-012-1119-7.
Ginting,R., Wanli., & Malik,A. (2020). Crude Palm Oil Product Quality Control Using Seven Tools (case study:XYZ Company). IOP Conf. Series: Materials Science and Engineering, 1-9, doi:10.1088/1757-899X/851/1/012046.
Giraldo, J.A., Castrillón, O.D., & Ruiz, S. (2019). Discrete Simulation and Agents of a Simple Chain Supply including a Geographic Information System (GIS). . Revista Información Tecnológica, 30(6), 123-136. Recuperado de http://dx.doi.org/10.4067/S0718-07642019000600123.
Giret, A., Trentesaux, D., & Prabhuc, V., (2015). Sustainability in manufacturing operations scheduling: a state of the art review. J. Manuf. Syst. 37, 126e140.
Glover,F & Kochenberger,G. (2010). Handbook of Metaheuristics. Boston: Springer. Recuperado de http://doi.org/10.1007/978-1- 4419-1665-5.
Goldberg,D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Boston: Addison-Wesley Longman Publishing.
Gómez, P. (2010). Programación de la producción en un taller de flujo híbrido sujeto a incertidumbre: arquitectura y algoritmos. España: Universidad Politécnica de Valencia.
Gong,G., Deng,Q., Gong,X., Liu, W., & Ren,Q. (2018). A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators. Journal of Cleaner Production, 174, 560- 576. Recuperado de https://doi.org/10.1016/j.jclepro.2017.10.188.
Gong,X., Deng,Q., Gong,G., Liu,W., & Ren,Q. (2017). A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility. International Journal of Production Research, 56 (7)., 2506–2522, Recuperhttps://doi.org/10.1080/00207543.2017.1388933.
Gong,V., Van der Wee,M., De Pessemier,T., Verbrugge,S., Colle,D., & Martens,L. (2017a). Energy- and labor-aware production scheduling for sustainable manufacturing: A case study on plastic bottle manufacturing. 24th CIRP Conference on Life Cycle Engineering, 2212-8271, doi: 10.1016/j.procir.2016.11.136.
Gracia,J.,Lara,L., Quintero,D., & Santis,A. (2018). Formulation of Strategies for the Implementation of Integral 14001:2015 in the Company Surtiapliques (Bogotá-Colombia). Chemical Engineering Transactions, 12, 559-564. doi: 10.3303/CET1867094.
Grossmann, E., Halemane, K., & Swaney, R. (1983). Optimization strategies for flexible chemical processes. Computers and Chemical Engineering, 7(4), 439-462. Recuperado de https://doi.org/10.1016/0098-1354(83)80022-2.
Gu,X., Huang,M., & Liang, X. (2020). A Discrete Particle Swarm Optimization Algorithm With Adaptive Inertia Weight for Solving Multiobjective Flexible Job-shop Scheduling Problem. IEEE Access, 8, 33125- 33136.
Guía País - Kenia. (2012). España: Ministerio de Asuntos Exteriores y Cooperación .
Gupta, S.M., 2016. Lean manufacturing, green manufacturing and sustainability. J. Japan Ind. Manag. Assoc. 67, 102–105. https://doi.org/10.11221/jima.67.102
Gutiérrez,C & García,I. (2011). Modular design of a hybrid genetic algorithm for a flexible job–shop scheduling problem. Knowledge-Based Systems, 24, 102–112. doi:10.1016/j.knosys.2010.07.010.
Hadka,D., & Reed,P. (2012). Diagnostic Assessment of Search Controls and Failure Modes in Many-Objective Evolutionary Optimization. Evolutionary Computation, 20(3), 423–452.
Haitham,S.,& and Deb,H. (2014). U-NSGA-III: A Unified Evolutionary Algorithm for Single,Multiple, and Many-Objective Optimization. Computational Optimization and Innovation Laboratory, 5(12), 1-30.
Hale,J., Legun,J., Campbell,H.,& Carolan,M. (2019). Social sustainability indicators as performance. Geoforum , 103, 47–55. Recuperado de https://doi.org/10.1016/j.geoforum.2019.03.008.
Haugeland, J. (1985). Artificial Intelligence: The Very Idea. Cambridge: MIT Press.
He, W., & Sun, D. (2012). Scheduling flexible job shop problem subject to machine breakdown with route changing and right-shift strategies. . The International Journal of Advanced Manufacturing Technology, 66(1-4), 501–514. http://doi.org/10.1007/s00170-012-4344-4.
He,Y., Li,Y., Wu,T., Sutherland,J. (2015). An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. Journal of Cleaner Production, 87, 245-254. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2014.10.006.
Herrero,M.,& Stuckey,D. (2015). Bioaugmentation and its application in wastewater treatment: A review. Chemosphere, 140 , 119–128. Recuperado de http://dx.doi.org/10.1016/j.chemosphere.2014.10.033.
Holcombe, H. (2018). Queer After All: Middlesex, the Ford Factory, and the Genetic History of Incest. Arizona Quarterly. Journal of American Literature, Culture, and Theory , 74(2), 1-37. doi:10.1353/arq.2018.0007.
Holland, J. H. (1975). Adaptation in natural and artificial systems.The University of Michigan Press, Englewood Cliffs, 66–72.
Hongbo,L.,, Ajith,A., Zuwen,W. (2009). A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems. Fundamenta Informaticae , 95 (4), 465-489.
Hongfeng,W., Yaping,F., Min,H., George,Q ., Huang,B ., Junwei,W. (2017). A NSGA-II based memetic algorithm for multiobjective parallel flowshop scheduling problem. Computers & Industrial Engineering, 113, 185–194. Recuperado de http://dx.doi.org/10.1016/j.cie.2017.09.009.
Hongtao,T., Rong,C,, Yibing,L., Zhao,P-. Shunsheng,G.,&Yuzhu,D. (2019). Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop. Applied Soft Computing Journal, 78, 176–194. Recuperado de https://doi.org/10.1016/j.asoc.2019.02.011.
Hormozi,H., Zhang, Z., Zarei,O., & Ramezanian, R. (2018). Trade-off between the costs and the fairness for a collaborative production planning problem in make-to-order manufacturing. Computers & Industrial Engineering, 126, 421-434. recuperado de https://doi.org/10.1016/j.cie.2018.09.044.
Horn, J., Nafpliotis, N., & Goldberg, D. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, Orlando, FL, 82-87. doi: 10.1109/ICEC.1994.350037.
Hossain,S., Galbreath.J., Monzur,M., & Randøy,T. (2020). Does competition enhance the double-bottom-line performance of microfinance institutions? Journal of Banking and Finance 113 (2020) 105765, 113, 1-17. Recuperdo de https://doi.org/10.1016/j.jbankfin.2020.105765.
Hosseinabadi, R., Siar, H., Shamshirband, S., Shojafar, M., & Nasir, M. (2014). Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterpris. Annals of Operations Research, 229(1), 451-474. doi: 10.1007/s10479-014-1770-8.
Hu,C., Dai, L., Yan,X., Gong,W., Liu,X., & Wang,L. (2020). Modified NSGA-III for sensor placement in water distribution system. Information Sciences, 509, 488–500. Recuperado de https://doi.org/10.1016/j.ins.2018.06.055.
Huang, J., & Süer, G. (2015). A dispatching rule-based genetic algorithm for multi-objective job shop scheduling using fuzzy satisfaction levels. Computers & Industrial Engineering, 86, 29–42. Recuperado de http://doi.org/10.1016/j.cie.2014.12.001.
Huang, R., Yang, C., & Cheng, W. (2013). Flexible job shop scheduling with due window-a two-pheromone ant colony approach. International Journal of Production Economics, 141(2), 685–697. Recuperado de http://doi.org/10.1016/j.ijpe.2012.10.011.
Huang, S., Tian, N., Wang, Y., & Ji, Z. (2016a). Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization. Springer Plus, 5 (1). doi: 10.1186/s40064-016-3054-z.
Huang, W., Zhao. Y. & Ma, X. (2014). An improved genetic algorithm for job-shop scheduling problem with process sequence flexibility. International . Journal of Simulation Modeling, 13(4), 510-522. Recuperado de http://m.ijsimm.com/Full_Papers/Fulltext2014/text.
Huang, X., & Yang, L. (2019). A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time. International Journal of Intelligent Computing and Cybernetics, 12 (2), 154-174.
Huang,D.,Tian,N.,& Ji, W. (2016b). Particle swarm optimization with variable neighborhood search for multiobjective flexible job shop scheduling problem. International Journal of Modeling, Simulation, and Scientific Computing, 7(3). Recuperado de https://doi.org/10.1142/S1793962316500240
Hui,L., Rongrong,Z., , Shi,C., & Yuhui,S. (2019). Multi-center variable-scale search algorithm for combinatorial optimization problems with the multimodal property. Applied Soft Computing Journal, 84, 105-116. Recuperado de https://doi.org/10.1016/j.asoc.2019.105726.
Huot, H., Séré, G., Charbonnier, P., Simonnot, M., & Morel, J. (2015). Lysimeter monitoring as assessment of the potential for revegetation to manage former iron industry settling ponds. The Science of the Total Environment, 526, 29–40. Recuperado de http://doi.org/10.1016/j.scitotenv.2015.04.025.
Hutchins,M., & Sutherland,J. (2008). An exploration of measures of social sustainability and their application to supply chain decisions. Journal of Cleaner Production, 16 (15), 1688-1698. Recuperado de https://doi.org/10.1016/j.jclepro.2008.06.001.
Hutchins,M., Richter,J., Henry,M., & Sutherland,J. (2019). Development of indicators for the social dimension of sustainability in a U.S. business context. Journal of Cleaner Production , 212, 687- 697. Recuperado de https://doi.org/10.1016/j.jclepro.2018.11.199.
Hutomo, M; Haizam., & Sinaga. (2018). The Mediating Role of Organizational Learning Capability On Green Distribution and Green Packaging TowardsSustainability Performance as A Function Environmental. Dynamism: Indonesia and Malaysia Fishery Industries. Conf. Series: Earth and Environmental Science , 164,1-12.
Ibrahim,A., Vargas,M., Rahnamayan,S., & Deb,K. (2016). EliteNSGA-III: An Improved Evolutionary Many-Objective Optimization Algorithm. WCCI, 11(7),1-10.
IEA. (2019). Emisiones globales de CO2 en 2019. Obtenido de https://www.iea.org/articles/global-co2-emissions-in-2019
Ishibuchi,H., & Murata. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 392-403. doi: doi: 10.1109/5326.704576.
Jain,V., Singhal,A., Sachdeva,G., & Kachhwaha. (224 (2020) 113348). Advanced exergy analysis and risk estimation of novel NH3-H2O and H2O-LiBr integrated vapor absorption refrigeration system. Energy Conversion and Management, 224, 2-16.
Jalali, T., Jafari,M.,& Mohammadi,A. (2018). Genetic algorithm optimization of antireflection coating consisting of nanostructured thin films to enhance silicon solar cell efficacy. Materials Science & Engineering B , 247, 1-8. Recuperado de https://doi.org/10.1016/j.mseb.2019.05.016.
Jarke,M., & Radermacher,F. (1988). The AI potential of model management and its central role in decision support. Decision Support Systems, 387-404, Recuperado de https://doi.org/10.1016/0167-9236(88)90002-4.
Jeyadebi,S., Baskar,S., Babula,S., & Iruthayarajan,W. (2011). Solving multiobjective optimal reactive power dispatch using modified NSGA-II. International Journal of Electrical Power & Energy Systems, 33(2), 219-228, Recuperado de https://doi.org/10.1016/j.ijepes.2010.08.017.
Jia, S., & Hu, Z. (2014). Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem. . Computers & Operations Research, 47, 11–26. Recuperado de http://doi.org/10.1016/j.cor.2014.01.010.
Jia, Y., & Liu, R. (2012). Study of the energy and environmental efficiency of the Chinese economy based on a DEA model. Procedia Environmental Sciences, 13, 2256–2263. Recuperado de http://doi.org/10.1016/j.proenv.2012.01.214.
Jia, Z., Zhu, J., & Chen, H. (2012). Dynamic tabu particle swarm optimization algorithm for flexible job-shop scheduling. Journal of South China University of Technology, 40(1). Recuperado de https://scholar.google.es/scholar?q=Dynamic+tabu+particle+swarm+optimization+algorithm+for+flexible+job-shop+scheduling++&btnG=&hl=en&as_sdt=0%2C5#0.
Jia,F., Yin,S., Chen,L.,& Chen,X. (2020). The circular economy in the textile and apparel industry: A systematic literature review. Contents lists available at Science Direct, 12(21), 1-20. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120728.
Jia,Z., Pei,M & Leung,J. (2017). Multi-objective ACO algorithms to minimise the makespan and the total rejection cost on BPMs with arbitrary job weights. International Journal of Systems Science, 48(16), 3542-3557. Recuperado de https://doi.org/10.1080/00207721.2017.1387314.
Jiang, Z., Zuo, L., & E, M. . (2014). Study on multi-objective flexible job-shop scheduling problem considering energy consumption. Journal of Industrial Engineering and Management, 7(3), 589–604. Recuperado de http://doi.org/10.3926/jiem.1075.
Jiang,E., & Wang,L. (2020). Multi-objective optimization based on decomposition for flexible job shop scheduling under time-of-use electricity prices. Knowledge-Based Systems, 204, 106- 177.
Jiang,M., Zeng,M., Cao, Z.,Bi,Y., Wang, L., Wang,Y. (2019). The history, logic and trends of the discipline of safety science in China. Safety Science , 116, 37–148. Recuperado https://doi.org/10.1016/j.ssci.2019.03.005.
Jiang,T & Deng,G. (2018). Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem Considering Energy Consumption. IEEE Access, 6, 46346- 46354. doi:0.1109/ACCESS.2018.2866133.
Jiao,Z., Yuan, S., Ji,C., Mannan,S., & Wang,Q. (2019). Optimization of dilution ventilation layout design in confined environments. Journal of Loss Prevention in the Process Industries, 60, 195–202, Recuperado de https://doi.org/10.1016/j.jlp.2019.05.002.
Joc, T., & Nhu, H. (2008). Evolving dispatching rules using genetic programming for solving multiobjective flexible job shop problems. Computers & Industrial Engineering , 54 (22), 453–473.
Jones,J., Bleasdale,S., Maita,D., & Brosseau,L. (2020). A systematic risk-based strategy to select personal protective equipment for infectious diseases. American Journal of Infection Control, 48, 46−51. Recuperado de https://doi.org/10.1016/j.ajic.2019.06.023.
Kacem, I., Hammadi, S., & Borne, P. (2002). Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation, 60(3-5), 245–276. recuperado de http://doi.org/10.1016/S0378-4754(02)00019-8.
Kaplanoğlu, V. (2016). An object-oriented approach for multi-objective flexible job-shop scheduling problem. Expert Systems with Applications, 45, 71–84. http://doi.org/10.1016/j.eswa.2015.09.050, 45, 71–84.
Karimi-Nasab, M., Modarres, M., & Seyedhoseini, S. (2015). A self-adaptive PSO for joint lot sizing and job shop scheduling with compressible process times. Applied Soft Computing, 27, 137–147. http://doi.org/10.1016/j.asoc.2014.10.015, 27, 137–147. Recuperado de http://doi.org/10.1016/j.asoc.2014.10.015.
Karthikeyan, S., Asokan, P., & Chandrasekaran, M. (2014a). A Hybrid Discrete Firefly Algorithm for Multi-Objective Flexible Job Shop Scheduling Problems with Maintenance Activity. Applied Mechanics and Materials, 575, 922–925. http://doi.org/10.4028/www.scientific.net/AMM.575.922.
Karthikeyan, S., Asokan, P., & Nickolas, S. (2014b). A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints. The International Journal of Advanced Manufacturing Technology, 72 (9), 9-12. Obtenido de http://doi.org/10.1007/s00170-014-5753-3.
Kay Camarillo, M., Stringfellow, W. T., Jue, M. B., & Hanlon, J. . (2012). Economic sustainability of a biomass energy project located at a dairy in California, USA. . Energy Policy, 48, 790–798. Recuperado de http://doi.org/10.1016/j.enpol.2012.06.020.
Kaya, S. & Figlali, N. (2013). Multi Objective Flexible Job Shop Scheduling Problems. Journal of Engineering and Natural Sciences, 31, 605-623. Recuperado de http://eds.yildiz.edu.tr/ArticleContent/Journal/sigma/Volumes/2013/Issues/Regular-4/YTUJENS-20134/YTUJENS-2013-31-4.485.pdf .
Kempen,E., Casas,M., Pershagen,G., & Foraster,M. (2018). WHO Environmental Noise Guidelines for the European Region: A Systematic Review on Environmental Noise and Cardiovascular and Metabolic Effects: A Summary. International Journal of Environmental Research and Public Health, 15 (379), 1.59. doi:10.3390/ijerph15020379.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks, 4, 1942–1948.
Khan,I. (2020). Critiquing social impact assessments: Ornamentation or reality in the Bangladeshi electricity infrastructure sector? Energy Research & Social Science , 60 (10), 1-15. Recuperado de https://doi.org/10.1016/j.erss.2019.101339.
Khosravi,F.,& Izbirak,G. (2019). A stakeholder perspective of social sustainability measurement in healthcare. Sustainable Cities and Society , 50, 1-12. Recuperado de https://doi.org/10.1016/j.scs.2019.101681.
Kiourtisa,A.,Nifakosb,S., Mavrogiorgoua,A., & Kyriazisa,D. (2019). Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching. International Journal of Medical Informatics, 132, 1-16. Recuperado de https://doi.org/10.1016/j.ijmedinf.2019.104002.
Korhonen, P.., & Laakso, J. (1986). A visual interactive method for solving the multiple criteria problem (VIA). European Journal of Operational Research, 24(2), 277-287. Recuperado de https://doi.org/10.1016/0377-2217(86)90050-0.
Kroppa,I., Nejadhashemia,A.,Deb,K., Aboualia,M., Royc,P., Adhikaria, U., & Hoogenboomd,G. (2019). A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification. Agricultural Systems, 173, 289–302. Recuperado de https://doi.org/10.1016/j.agsy.2019.03.014.
Kudryavtsev,S., Yemelin,P., & Yemelina,N. (2018). The Development of a Risk Management System in the Field of Industrial Safety in the Republic of Kazakhstan. Safety and Health at Work, 9, 30-41. Recuperado de http://dx.doi.org/10.1016/j.shaw.2017.06.003.
Kumar,A., & Anbanandam,R. (2019). Development of social sustainability index for freight transportation system. Journal of Cleaner Production, 210, 77- 92. Recuperado de https://doi.org/10.1016/j.jclepro.2018.10.353.
Kvam,P., & Hintze,A. (2018). Rewards, risks, and reaching the right strategy: Evolutionary paths from heuristics to optimal decisions. Research Gate, 1-13. Recuperado de https://www.researchgate.net/publication/321106854.
Kyoung,M., Jin,Y. (2017). Exposure to environmental noise and risk for male infertility: A population-based cohort study. Environmental Pollution, 226: 118. doi 10.1016/j.envpol.2017.03.069. Lacomme, P., Larabi, M., & Tchernev, N. (2013). Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Economics, 143(1), 24–34. recuperado de http://doi.org/10.1016/j.ijpe.2010.07.012.
Laosiritaworn,W., Pinchai, N., & Parupuncharadsri,N. (2020). Job Shop Layout Improvement with Simulation Technique: A Case of. IOP Conference Series: Materials Science and Engineering, 1-6. Laukkanen,M. (2019). Sustainable business models for advancing system-level sustainability. London: Acta Universitatis.
Lavicoli,A., Gambelunghe , A., Magrini, G., Mosconi d, L., Soleo, L., Vigna, A., Trevisan, A., Bruno, M., Chiambretti,M., Scarpitta, L., Sciacca, U & Valentini. (2019). Diabetes and work: The need of a close collaboration between diabetologist and occupational physician. Nutrition, Metabolism & Cardiovascular Diseases, 29, 220-227. Recuperado de https://doi.org/10.1016/j.numecd.2018.10.012.
Lei, D., & Guo, X. (2015). An effective neighborhood search for scheduling in dual-resource constrained interval job shop with environmental objective. International Journal of Production Economics, 159, 296–303. Recuperado de http://doi.org/10.1016/j.ijpe.2014.07.026.
Lei,D., Li,M., & Wang,L. (2019). A Two-Phase Meta-Heuristic for Multiobjective Flexible Job Shop Scheduling Problem With Total Energy Consumption Threshold. IEEE Transactions on Cybernetics , 49(3), 1097-1109,doi: 10.1109 / TCYB.2018.2796119.
Leyland, G. (2002). Multi-objective optimisation applied to Industrial Energy Problems. Auckland - Nueva Zelanda: Universidad de Auckland.
Li, C., Cui, H., & Wang, G. (2013a). The Optimization of Flexible Job-Shop Scheduling Problem Based on NSGA-II. Advanced Materials Research, 651(3), 684-687. Recuperado de http://www.scientific.net/AMR.651.684.
Li, J., & Pan, Q. (2013). Chemical-reaction optimization for solving fuzzy job-shop scheduling problem with flexible maintenance activities. International Journal of Production Economics, 145(1), 4–17. Recuperado de http://doi.org/10.1016/j.ijpe.2012.11.005.
Li, J., Duan, P., Cao, J., X., Lin,X.,& Han,Y. (2018a). A Hybrid Pareto-Based Tabu Search for the Distributed Flexible Job Shop Scheduling Problem With E/T Criteria. IEEE Access, 58883-58897, doi: 10.1109/ACCESS.2018.2873401.
Li, J., Pan, Q., & Liang, Y. (2010a). An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 59(4), 647–662. Recuperado de http://doi.org/10.1016/j.cie.2010.07.014.
Li, J., Pan, Q., & Xie, S. (2012a). An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Applied Mathematics and Computation, 218(18), 9353–9371. Recuperado de http://doi.org/10.1016/j.amc.2012.03.018.
Li, J.-Q., Pan, Q.-K., & Chen, J. (2012b). A hybrid Pareto-based local search algorithm for multi-objective flexible job shop scheduling problems. International Journal of Production Research, 50(4), 1063–1078. Recuperado de http://doi.org/10.1080/00207543.2011.555427.
Li, J.-Q., Pan, Q.-K., & Tasgetiren, M. (2014). A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Applied Mathematical Modelling, 38(3), 1111–1132. Recuperado de http://doi.org/10.101.
Li, L., & Huo, J. (2009). Multi-Objective Flexible Job-Shop Scheduling Problem in Steel Tubes Production. Systems Engineering - Theory & Practice, 29(8), 117–126. Recuperado de http://doi.org/10.1016/S1874-8651(10)60063-4.
Li,B., Guo, C.,& Ning, T. (2018b). An improved bacterial foraging optimization for multi-objective flexible job-shop scheduling problem(Article). Journal Europeen des Systemes Automatises, 51(8),323-332.
Li,H., Zhu,H., & Jiang,T. (2020a). Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem. Algorithms , 23(12), 13-44. recuperado de doi:10.3390/a13020044.
Li,J., Deng,J., Li,C., Han,Y., Tian,J., Zhang,B.,& Wang,C. (2020b). An improved Jaya algorithm for solving the flexible job shop scheduling problem with transportation and setup times. Knowledge-Based Systems, 200.1-12. Recuperado de https://doi.org/10.1016/j.knosys.2020.106032.
Li, J., & Pan, Q. (2012). Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity. Applied Soft Computing, 12(9), 2896–2912. Recuperado de http://doi.org/10.1016/j.asoc.2012.04.012.
Li, J., Pan,Q., & Gao,K. (2011a). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Technol, 4(5), :1159–1169.
Li, J., Paq,Q., Xie,S., Wang,S. (2011b). A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems. Int. J. of Computers, Communications & Control, 6(2), 286-296.
Li, J., Pan,Q., Wang,F. (2014a). A hybrid variable neighborhood search for solving the hybrid flowshop scheduling problem. Applied Soft Computing , 24(12), 63–77.
Li,U.,Pan,Q., Wang, Y. (2010b). Hybrid Pareto-based tabu search algorithm for solving the multi-objective flexible Job Shop scheduling problem. Computer Integrated Manufacturing Systems, 16(7), 1419-1426.
Li,X., Peng,Z., Dub, B., Guo,J., Xu,W., Zhuang, K . (2016). Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems. Computers & Industrial Engineering, 113 (2017) 10–26. Recuperdo de http://dx.doi.org/10.1016/j.cie.2017.09.005.
Li,X.,& Gao,L. (2016). An effective hybrid genetic algorithm and tabu searchfor flexiblejob shop schedulingproblem. Int. J.Production Economics, 174, 93–110. Recuperado http://dx.doi.org/10.1016/j.ijpe.2016.01.016.
Li,Y., He,Y., Wang,Y.,Tao,F., & Sutherland,J. (2020c). An optimization method for energy-conscious production in flexible machining job shops with dynamic job arrivals and machine breakdowns. Journal of Cleaner Production , 254, 1- 14. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120009.
Li,Z., Qian,B., Hu,R., Chang,L.,& Yang,J. (2019). An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups. Knowledge-Based Systems, 173, 83–112.
Lin,J., Pan,Q., & Liang,Y. (2010). An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 59, 647–662.
Liu, A., Ya, Yang., Xing, Q., & Lu, H. (2011a). Dynamic scheduling on multi-objective flexible Job Shop. Computer Integrated Manufacturing Systems, 17(12), 2629-2637.
Liu, A., Ya, Yang., Xing, Q., Lu, H., & Zhang, Y. (2011b). Multi-population genetic algorithm in multi-objective fuzzy and flexible Job Shop scheduling. Computer Integrated Manufacturing Systems,17(9), 1954-1961.
Liu, B., Fan, Y., & Liu, Y. (2015a). A fast estimation of distribution algorithm for dynamic fuzzy flexible job-shop scheduling problem. Computers & Industrial Engineering, 87, 193–201. Recuperado de http://doi.org/10.1016/j.cie.2015.04.029 .
Liu, Q., Tian,Y., Wang, C., Chekem, F., & Sutherland, J. (2018). Flexible job-shop scheduling for reduced manufacturing carbon footprint. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 140(6), 21-39.
Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2015b). Reducing environmental impact of production during a Rolling Blackout policy – A multi-objective schedule optimisation approach. Journal of Cleaner Production, 102, 418–427. Recuperado de http://doi.org/10.1016/j.jclepro.2015.04.038.
Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2016). A multi-objective genetical objective genetic algorithm for optimisation of energy consumption and shop floor production performance. Int. J. Production Economics, 179(2016)259–272. Recuperado de http://dx.doi.org/10.1016/j.ijpe.2016.06.019.
Liu,D., Huang,Q., Yang,Y., Liu, D.,& Wei,X. (2020). Bi-objective algorithm based on NSGA-II framework to optimize reservoirs operation. Journal of Hydrology, 585, 1-12. Recuperado de https://doi.org/10.1016/j.jhydrol.2020.124830.
Liu,H., Chena,C., Lv,X., Wuc,X ., & Liud,M. (2019). Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Conversion and Management, 195 , 328–345. Recuperado de https://doi.org/10.1016/j.enconman.2019.05.020.
Liu,N., Zhang,Y., & Feng,W. (2019b). Improving Energy Efficiency in Discrete Parts Manufacturing System Using an Ultra Flexible Job Shop Scheduling Algorithm. International Journal of Precision Engineering and Manufacturing-Green Technology, 6:349–365. recuperado de https://doi.org/10.1007/s40684-019-00055-y.
Liu,Q., Zhan,M .,Chekem,F.,Shao,X .,Ying,B., & Sutherland,J. (2017). A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint. Journal of Cleaner Production, 168, 668-678. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2017.09.037.
Liu,T., Chen,Y.,& Chou,H. (2014). Developing a Multiobjective Optimization Scheduling System for a Screw Manufacturer: A Refined Genetic Algorithm Approach. IEEE Aceess, 2(11),356- 364.
Liu,Y.,Dong,H., Lohse,N., & Petrovic,S. (2016). A multi-objective genetic algorithm fo roptimisation of energy consumption and shop floor production performance. Int. J.ProductionEconomics, 179(, 259–272. Obtenido de http://dx.doi.org/10.1016/j.ijpe.2016.06.019.
Liu, Z., Guo,S., & Wang,L. (2019a). Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. Journal of Cleaner Production, 211, 765- 786. Recuperado de https://doi.org/10.1016/j.jclepro.2018.11.231.
Liu, Z., Yan,J., Cheng,Q., Yang,C., & Sun,S. (2020). The mixed production mode considering continuous and intermittent processing for an energy-efficient hybrid flow shop scheduling. Journal of Cleaner Production , 246, 1-17. Recuperado de https://doi.org/10.1016/j.jclepro.2019.119071.
Liu, Q., Panb,Q., Gaoa,L., & Lia,X. (2019). Multi-Objective Flexible Job Shop Scheduling Problem Considering Machine Switching Off-On Operation. Procedia Manufacturing, 39, 1167–1176.
López, M., Díaz Flores.,& Winkler,R. (2018). Increase of peroxidase activity in tropical maize after recurrent selection to storage pest resistance. Journal of Stored Products Research, 75, 47- 55. Recuperado de https://doi.org/10.1016/j.jspr.2017.11.007.
Lozano, S., & Calzada, L. (2019). Efficiency ranking using dominance network and multiobjective optimization indexes. Expert Systems with Applications, 126, 83-91. Recuperado de https://doi.org/10.1016/j.eswa.2019.02.016.
Lu, C., Li, X., Gao, L., Liao, W., & Yi, J. (2017). An effective multi-objective discrete virus optimization algorithm for flexible job-shop scheduling problem with controllable processing times. Computers & Industrial Engineering , 104, 156–174. Recuperado de http://dx.doi.org/10.1016/j.cie.2016.12.020.
Lu, K., Ting, L., Keming, W., Hanbing, Z., Makoto, T., & Bin, Y. (2015). An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem. Algorithms. Algorithms, 8(1), 19-3. http://www.mdpi.com/1999-4893/8/1/19/htm.
Lu, Y., & Zhang, X. (2015). Corporate sustainability for architecture engineering and construction (AEC) organizations: Framework, transition and implication strategies. Ecological Indicators, 61, 911–922. http://doi.org/10.1016/j.ecolind.2015.10.046.
Luger, F & Stubblefield, W. (1993). Artificial Intelligence Structures and Strategies for Complex Problem Solving. Palo Alto: Benjamin Cummings.
Luger, G. (2002). Artificial Intelligence: Structures and Strategies for Complex Problem Solving . London: Addison-Wesley Longman.
Lui, Q., Li, X., Liu, H., & Guo, Z. (2020). Multi-objective metaheuristics for discrete optimization problems: A review of the state-of-the-art. . Applied Soft Computing Journal, , 93, 1-16. Recuperado de https://doi.org/10.1016/j.asoc.2020.106382.
Luo,D., Chen,H.,Wu,S., Shi, Y. (2010). Hybrid ant colony multi-objective optimization for flexible job shop scheduling problems. Journal of Internet Technology, 11(3), 361-370.
Luo,S., Zhang,L., & Fan,Y. (2019). Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. Journal of Cleaner Production 234 (2019), 1365-1384. Recuperado de https://doi.org/10.1016/j.jclepro.2019.06.151.
Macias,S., & Sakao,T. (2020). Effective ecodesign implementation with the support of a lifecycle engineer. Journal of Cleaner Production, 279, 1-10.
Magazzino, C., Mele, M., Schneider, N., & Sarkodie, S.A., 2020. Waste generation, Wealth and GHG emissions from the waste sector: Is Denmark on the path towards Circular Economy? Sci. Total Environ. 755, 142510. https://doi.org/10.1016/j.scitotenv.2020.142510
Maes,P. (1993). Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back. London: Firet MIT Press edition .
Mahmud,M.,Abidina,A., Mohamed,Z.,Rahman,M., & Lida,M. (2019). Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment. Computers and Electronics in Agriculture, 157, 488-499. recuperado de https://doi.org/10.1016/j.compag.2019.01.016.
Mahzun, R., & Kalalo, F. (2019). The environmental aspect and impact assessment for heavy industries: Empirical study on steel fabrication and shipyard operations in batam Indonesia. Quality - Access to Success, 20 (172), 108-113.
Maiera,H., Razavib,S., Kapeland,Z., Matotte,L., & Kasprzykf, B. (2019). Introductory overview: Optimization using evolutionary algorithms and other metaheuristics. Environmental Modelling and Software , 114 (, 195–213. Recuperado de https://doi.org/10.1016/j.envsoft.2018.11.018.
Malek,J & Desai,T. (2020). A systematic literature review to map literature focus of sustainable manufacturing. Journal of Cleaner Production, 256,1-20. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120345.
Mandal,P., Perumal,G., Arora,H., & Ghosh,S. (2020). Green manufacturing of nanostructured Al-Based sustainable selfcleaning metallic surfaces. Journal of Cleaner Production, 278, 1-10.
Mani, V., Agrawal, R., & Sharma, V. (2015). Social sustainability practices in the supply chain of Indian manufacturing industries. International Journal of Automation and Logistics,, 1(3), 211 - 233.
Mani,V., Chiappetta,C.,& Mani,J. (2020). Supply chain social sustainability in small and medium manufacturing enterprises and firms’ performance: Empirical evidence from an emerging Asian economy. Int. J. Production Economics , 227, 1-13.
Maqrini,H & Teghem,H. (1995). Scheduling complex flexible job shop problems. Proceedings INRIA/IEEE Symposium on Emerging Technologies and Factory Automation. ETFA95, Paris, France, 541-549, doi: 10.1109/ETFA.1995.496806.
Marimin, M., & Farhan, M. (2020). Sustainable flexible flow shop scheduling optimization in flexible packaging industry using genetic algorithm. IOP Conf. Series. Earth and Environmental Science, 472, 012050. doi:10.1088/1755-1315/472/.
Marimin, Darmawan, M.A., Widhiarti, R.P., & Teniwut, Y.K., 2018. Green productivity improvement and sustainability assessment of the motorcycle tire production process: A case study. J. Clean. Prod. 191, 273–282. https://doi.org/10.1016/j.jclepro.2018.04.228
Marqués,M. (2017). Sostenibilidad, comunicación y valor compartido: el discurso actual del desarrollo sostenible en la empresa española (Tesis Doctoral). Madrid: Universidad Complutense de Madrid.
Martínez,J. (2017). Aplicación de los instrumentos Brief y Best en la detención de riesgo ergonómico en la industria metalmecánica. Revista Tog, 14(26), 374-385
Martínez León, H.C., & Calvo-Amodio, J., 2017. Towards lean for sustainability: Understanding the interrelationships between lean and sustainability from a systems thinking perspective. J. Clean. Prod. 142, 4384–4402. https://doi.org/10.1016/j.jclepro.2016.11.132
Martínez,S., Pérez,E., Eguía, P.,Erkorekab,A., & Granada,E. (2020). Model calibration and exergoeconomic optimization with NSGA-II applied to a residential cogeneration. Applied Thermal Engineering, 169, 1-14. Recuperado de https://doi.org/10.1016/j.applthermaleng.2020.114916.
May, G., Stahl, B., Taisch, M., & Prabhu, V. (2015). Multi-objective genetic algorithm for energy-efficient job shop scheduling. International Journal of Production Research, 53(23), 7071-7089. Retrieved from http://www.tandfonline.com/doi/abs/10.1080/002.
May, G., Stahl, B., Taisch, M., & Kiritsis, D., 2017. Energy management in manufacturing: from literature review to a conceptual framework. J. Clean. Prod. 167, 1464e1489.
McCarthy. (1959). Programs With Common Sense. Computer Science Department. Stanford University, 1-15.
McCarthy, J. (1956). Dartmouth Summer Research Conference on Artificial Intelligence. Dartmouth College.
Meiting,L., & Hua,W. (2020). Angular or rounded? The effect of the shape of green brand logos on consumer perception. Journal of Cleaner Production, 279, 1-7.
Mekni, S., & Chaâr, B. (2014). An enhanced PSO-based algorithm for multiobjective flexible job shop scheduling problems(FJSSPs). Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems - MEDES, 14 (12), 178–182. Recuperado de http://doi.org/10.1145/2668260.2668289.
Melchio, C., & Henriqson, E. (2020). Systematic Literature Review on Sustainability Metrics. Rev. FSA, 17(2), 24–39. Recuperado de http://www4.fsanet.com.br/revista/index.php/fsa/article/view/1930.
Meng,L., Zhang,C. Shao,X., & Ren,Y. (2019). MILP models for energy-aware flexible job shop scheduling problem. Journal of Cleaner Production, 210, 710- 723. Recuperado de https://doi.org/10.1016/j.jclepro.2018.11.021.
Min Liu a, Xifan Yao a,∗, Yongxiang Li a,b. (2020). Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems. Applied Soft Computing Journal , 87, 1-16. Recuperado de https://doi.org/10.1016/j.asoc.2019.105954.
Min, H., & Choi, S.B. (2019). Green sourcing practices in Korea. Manag. Res. Rev. 43, 1–18. https://doi.org/10.1108/MRR-11-2018-0446
Dai,M., Dunbing,T., Giret,A.,& Salido,M. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer Integrated Manufacturing, 59 (2019) 143–157. Recuperado de https://doi.org/10.1016/j.rcim.2019.04.006.
Minambiente. (2019). Estudio Nacional del agua. Obtenido de http://documentacion.ideam.gov.co/openbiblio/bvirtual/023858/ENA_2018.pdf
Ministerio para la Transición Ecologica y el Reto Demográfico. (2020). Registro de huella de carbono, compensación y proyectos de absorción de dióxido de carbono. España.
Minsky,M. (1985). Communication with Alien Intelligence. Byte: Artificial Intelligence, 10(4), 127-138.
Minsky,M. (1961). Steps Toward Artificial Intelligence. Proceedings Of The Ire, 8-30.
Mohammadi,S., Al-e-Hashem,M., & Rekik,Y. (2020). An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company. International Journal of Production Economics, 219, 347–359. Recuperado de https://doi.org/10.1016/j.ijpe.2019.05.017.
Mokhtari, H., & Dadgar, M. (2015). Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate. Computers & Operations Research, 61, 31–45. Recuperado de http://doi.org/10.1016/j.cor.2015.02.014.
Mokhtari,H & Hasani,A. (2017). An energy-efficient multi-objective optimization for flexible job-shopscheduling problem. Computers and Chemical Engineering, 104 (2017) 339–352. Recuperado de http://dx.doi.org/10.1016/j.compchemeng.2017.05.004.
Moniz,D., Pedro,J., Horta,N., & Pires,J. (2019). Multi-objective framework for cost-effective OTN switch placement using NSGA-II with embedded domain knowledge. Applied Soft Computing Journal, 83, 1-12. recuperado de https://doi.org/10.1016/j.asoc.2019.105608.
Montalban,L., García,L., Sanz,A., & Pellicer,E. (2018). Social sustainability criteria in public-work procurement: An international perspective. Journal of Cleaner Production , 198, 1355- 1371. Recuperado de https://doi.org/10.1016/j.jclepro.2018.07.083.
Moon, J., & Park, J. (2014). Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage. International Journal of Production Research, 52(13), 3922-3939. Recuperado de http://www.tandfonline.com/doi/abs/10.1080/00207543.2013.860251.
Moore,L., Wurzelbacher,S., & Shockey,T. (2018). Workers compensation insurer risk control systems: Opportunities for public health collaborations. Journal of Safety Research, 66, 141–150.Recuperado de https://doi.org/10.1016/j.jsr.2018.07.004.
Moradi,S., Fatemi,G., & Zandieh,M. (2011). Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem. Expert Systems with Applications, 38, 7169–7178.
Morais,D.,& Silvestre,D. (2018). Advancing social sustainability in supply chain management: Lessons from multiple case studies in an emerging economy. Journal of Cleaner Production, 199, 222-235. Recuperado de https://doi.org/10.1016/j.jclepro.2018.07.097.
Moslehi,G., & Mahnam,M. (2011). A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int. J. Production Economics, 12(9), 14–22.
Mülling,D., Land,A., Seuring,S., & Machado, L. (2018). Linking sustainability-oriented innovation to supply chain relationship integration. Journal of Cleaner Production, 172, 3448-3458. Recuperado de https://doi.org/10.1016/j.jclepro.2017.11.091.
Murphy,Y., Brennan, E., & Flessner,E. (2019). Anxiogenic parenting practices as predictors of pediatric body-focused repetitive behaviors. Journal of Obsessive-Compulsive and Related Disorders , 21, 46–54. Recuperado de https://doi.org/10.1016/j.jocrd.2018.12.002.
Muth, J. F., & Thompson, G. L. (1963). Industrial Scheduling, Prentice Hall, Englewood Cliffs, New Jersey, Ch 15, 225-251.
Naciones Unidas. (2015). Convención Marco sobre el Cambio Climático. Paris.
Ning, T.,& Jin,H. (2018). A cloud based improved method for multi-objective flexible job-shop scheduling problem. Journal of Intelligent and Fuzzy Systems, 35 (12), 823-829.
Nitisiria,K., Gena,M., & Ohwadaa,M. (2019). A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling. Computers & Industrial Engineering, 130, 381–394. Recuperado de https://doi.org/10.1016/j.cie.2019.02.035.
Norma GT 45. (2015). Instituto Colombiano de Normas Técnicas y Certificación. Guía para la identificación de los peligros y la valoración de los riesgos en seguridad y salud ocupacional. Bogotá: Icontec.
Noticias ONU. (2018). El Acuerdo de París sobre cambio climático no es suficiente. Obtenido de https://news.un.org/es/interview/2018/09/1441622
Noticias ONU. (2019). Termina la COP25 con pocos avances en cuanto a la reducción de emisiones de carbono. Obtenido de https://news.un.org/es/story/2019/12/1466671
Noticias ONU. (2020). El cambio climático es más mortal que el coronavirus. Obtenido de https://news.un.org/es/story/2020/03/1470901
Nouiri, M., Bekrar,A., & Trentesaux.D. (2018). Towards Energy Efficient Scheduling and Rescheduling for Dynamic Flexible Job shop problem. IFAC (International Federation of Automatic Control) Papers On Line , 51(11), 1275–1280. doi: 10.1016/j.ifacol.2018.08.357.
Nouiri,M., Bekrar,A., Jemai,A., Niar,S., & Ammari,A. (2018a). An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J Intell Manuf, 29, 603–615. Recuperado de https://doi.org/10.1007/s10845-015-1039-3.
Nouri,H. (2016). Development of a comprehensive model and BFO algorithm for a dynamic cellular manufacturing system. AppliedMathematicalModelling, 40, 1514–1531. Recuperado de http://dx.doi.org/10.1016/j.apm.2015.09.004.
NTC ISO 14001. (2015). Sistemas de Gestión Ambiental. Requisitos con orientación para su uso. Bogotá: Icontec. Instituto Colombiano de Normas Técnicas y Certificación .
Ojstersek, R., Lalic, D., Buchmeister, B. (2019). A new method for mathematical and simulation modelling interactivity: A case study in flexible job shop scheduling. Advances in Production Engineering & Management, 14(4), 435–448.
Okoshia,C., Limab,E., & Gouvea,E . (2019). Performance cause and effect studies: Analyzing high performance manufacturing companies. International Journal of Production Economics, 210, 27–41. Recuperado de https://doi.org/10.1016/j.ijpe.2019.01.003.
Onyeocha, C. (2014). Robust Production & Inventory Control Systems for Multi-product Manufacturing Flow Lines. Universidad de Dublín.
Ouoba,Y. (2017). Economic sustainability of the gold mining industry in Burkina Faso. Resources Policy, 51, 194–203. Recuperado de http://dx.doi.org/10.1016/j.resourpol.2017.01.001.
Owais,M., & Osman,M. (2018). Complete hierarchical multi-objective genetic algorithm for transit network design problem. Expert Systems With Applications, 114, 143–154.
Ozcan,S., & Simsir,F. (2019). A new model based on Artificial Bee Colony algorithm for preventive maintenance with replacement scheduling in continuous production lines. Engineering Science and Technology, an International Journal , 22, 1175–1186.
Ozturk,G., Bahadir,O., & Teymourifar,A. (2018). Extracting priority rules for dynamic multiobjective flexible job shop scheduling problems using gene expression programming. International Journal of Production Research, 57(19), 3121–3137.
Pagell, M., & Shevchenko, A. (2014). Why research in sustainable supply chain management should have no future. Development of Truly Sustainable Supply Chains, 50(1), 44–55.
Paik,S., & Zalk,D. (2019). A Simple Proposition for Improving Industrial Hygiene Air Sampling Methods. Safety and Health at Work , 10, 389- 392. Recuperado de https://doi.org/10.1016/j.shaw.2019.07.001.
Pajares,G & Santos,M. (2006). Inteligencia artificial e inteligencia del conocimiento . México: Alfaomega.
Palacios, J. J., González, M. A., Vela, C. R., González, I., & Puente, J. (2015). Genetic tabu search for the fuzzy flexible job shop problem. Computers & Operations, 54, 74–89. http://doi.org/10.1016/j.cor.2014.08.023.
Pandian, P., Sankar,S., Ponnambalam & Raj,V. (2012). Scheduling of Automated Guided Vehicle and Flexible Jobshop using Jumping Genes Genetic Algorithm. American Journal of Applied Sciences, 9 (10), 1706-1720.
Pehrsson, L. (2013). Manufacturing Management And Decision support using simulation-based Multi-objective Optimisation. Suecia: Universidad de Skovde.
Pérez,R., & Hernández,A. (2018). A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility. Applied Intelligence, 48:3707–3734. Recuperado de https://doi.org/10.1007/s10489-018-1160-z.
Pezzellaa,F., Morgantia, G., Ciaschettib,F. (2008). A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers & Operations Research, 35, 3202 – 3212. doi:10.1016/j.cor.2007.02.014.
Pinedo, M. (2005). Planning and scheduling in manufacturing and services. Vol. 24. New York: Springer.
Pinedo, M. (2016). Scheduling: Theory, Algorithms and Systems. Fifth Edition. New York: Springer.
Pires,J., Ferreira,A.,Bartocci,L., & Pinheiro,D. (2019). Systemic indicator of sustainable development: Proposal and application of a framework. Journal of Cleaner Production 241 (2019) 118383, 241,1-10. Recuperado de https://doi.org/10.1016/j.jclepro.2019.118383.
Piroozfard, H., Wong,K., & Peng, W. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling, 128, 267–283. Recuperado de http://dx.doi.org/10.1016/j.resconrec.2016.12.001.
PNUD. (2016). Objetivos de Desarrollo Sostenible. Herramientas de aproximación al contexto local. Obtenido de https://www.undp.org/content/dam/colombia/docs/ODM/undp-co-ODSColombiaVSWS-2016.pdf
Popovic a,T., Barbosa,A., Kraslawski,A.,& Carvalho,A. (2018). Quantitative indicators for social sustainability assessment of supply. Journal of Cleaner Production, 180, 748- 768. Recuperado de https://doi.org/10.1016/j.jclepro.2018.01.142.
Pradhan, M & Panda,G. (2017). Information Combining Schemes for Cooperative Spectrum Sensing: A Survey and Comparative Performance Analysis. Wireless Pers Commun, 94, 685–711. doi: 10.1007/s11277-016-3645-6.
Purshouse,R. (2003). On the Evolutionary Optimisation of Many Objectives. The University of Sheffield.
Quezada,W., Hernández,G., González,E., Rodríguez,R., & Molina,F. (2018). Gestión de la tecnología y su proceso de transferencia en Pequeñas y Medianas Empresas metalmecánicas del Ecuador. Ingeniería Industrial, 3(8), 303-314. Recuperado de http://www.rii.cujae.edu.cu.
Quizá, R. (2004). Optimización multiobjetivo del proceso de torneado. Cuba: Universidad de Matanzas .
Rabiee , M., Zandieh & Ramezani. (2012). Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. International Journal of Production Research, 50 (24), 7327–7342.
Rahmani, A., Siar, H., Shamshirband, S., Shojafar, M., & Nasir, M. (2015). Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises. Annals of Operat, 229(1), 451-474.
Rahmati, S., Zandieh, M., & Yazdani, M. (2012). Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 64(5-8), 915–932. Recuperado de http://doi.org/10.1007/s00170-012-4051-1.
Rahmati,S., Ahmadi,A.,& Karimi,B. (2018). Multi-objective evolutionary simulation based optimization mechanism for a novel stochastic reliability centered maintenance problem. Swarm and Evolutionary Computation, 40, 255–271. Recuperado de https://doi.org/10.1016/j.swevo.2018.02.010.
Ramachandrana,S.,Jayalal, M., Riyas,A., Jehadeesan, R & Devan,K. (2020). Application of genetic algorithm for optimization of control rods positioning in a fast breeder reactor core. Nuclear Engineering and Design , 361, 1-14. Recuperado de https://doi.org/10.1016/j.nucengdes.2020.110541.
Ramanathan, T., & Ting, Y. (2015). Selection of wet digestion methods for metal quantification in hazardous solid wastes. Journal of Environmental Chemical Engineering, 3(3), 1459–1467. Recuperado de http://doi.org/10.1016/j.jece.2015.05.006.
Ramicic,M., & Bonarini,A. (2020). Adaptation of learning agents through artificial perception. Adaptive Behavior, 28(2),79-89.
Reddy, S., Ratnam, Ch., Rajyalakshmi, G., & Manupati, V.K. (2018). An effective hybrid multi objective evolutionary algorithm for solving realtime event in flexible job shop scheduling problem. Measurement , 114 (20) 78–90. Recuperado de https://doi.org/10.1016/j.measurement.2017.09.022.
Rena,W., Wena,J., Hua,Y., & Lib,J. (2020). Maintenance service network redesign for geographically distributed moving assets using NSGA-II in agriculture. Computers and Electronics in Agriculture , 169. Recuperado de https://doi.org/10.1016/j.compag.2019.105170.
Renna, P. (2012). Controllable processing time policies for job shop manufacturing system. . The International Journal of Advanced Manufacturing Technology, 67(9-12), 2127–2136. http://doi.org/10.1007/s00170-012-4635-9.
Resoloución A/68/970. (2014). Informe del Grupo de Trabajo Abierto de la Asamblea General sobre los Objetivos de Desarrollo Sostenible. Ginebra: Naciones Unidas.
Resolución 2734 . (2010). “Por la cual se adoptan los requisitos y evidencias de contribución al desarrollo sostenible del país y se establece el procedimiento para la aprobación nacional de proyectos de reducción de emisiones de gases de efecto invernadero que optan al Mecanismo . Bogotá: Ministerio de Ambiente, Vivienda y Desarrollo Territorial .
Pontes,R., Ferreira,A., & Custodio,D. (2020). Application of Hybrid Simulation in production scheduling in job shop systems Modeling and Simulation International. Simulation: Transactions of the Society for, 96(3), 253-268.
Rohaninejad, M., Sahraeian, R., & Nouri, B. (2016). Multi-objective optimization of integrated lot-sizing and scheduling problem in flexible job shops. Operations Research, 50(3), 587-609. http://dx.doi.org/10.1051/ro/2015049.
Romagnoli,G. (2015). Design and simulation of CONWIP in the complex flexible job shop of a Make-To-Order manufacturing firm. International Journal of Industrial Engineering Computations, 6, 117–134, doi:10.5267/j.ijiec.2014.8.003.
Rong-Hwa., Tung-Han,Y. (2017). An effective ant colony optimization algorithm for multi-objectivejob shop scheduling with equal-size lot-splittingRong. Applied Soft Computing, 57, 642–656. Recuperado de http://dx.doi.org/10.1016/j.asoc.2017.04.062.
Rossi,A.,& Dini,G. (2007). Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method. Robotics and Computer-Integrated Manufacturing, 23, 503–516. doi:10.1016/j.rcim.2006.06.004.
Rosso,F., Ciancio,F.,Dell’Olmo,J., & Salata, F. (2020). Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application. Energy & Buildings , 216, 1-18. Recuperado de https://doi.org/10.1016/j.enbuild.2020.109945.
Ruan, J., & Xu, Z. (2016). Constructing environment-friendly return road of metals from e-waste: Combination of physical separation technologies. Renewable and Sustainable Energy Reviews, 54, 745–760. Recuperado de http://doi.org/10.1016/j.rser.2015.10.114.
Rubaiee, S., & Yildirim, M.B., 2019. An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling. Comput. Ind. Eng. 127, 240–252. https://doi.org/10.1016/j.cie.2018.12.020
Ruiz, M.R., Briceño, L.J., Severiche, C.A., & Durán, L. (2019). Legal framework of environmental management for agricultural SMEs: Caribbean Colombian Context. . Revista Espacios, 40(32), 9-15. Recuperado de https://www.revistaespacios.com/a19v40n32/a19v40n32p09.pdf.
Ruiz, S. (2015). Metodología multiobjetivo basada en un comportamiento evolutivo para programar sistemas de producción flexible job shop. Aplicaciones en la industria metalmecánica. Universidad Nacional de Colombia.
Russell, S., & Norvig, P. (2004). Inteligencia Artificial: un enfoque moderno. México: Printice Hall.
Saad,M., Nazzala,N., & Darrasa,B. (2019). A general framework for sustainability assessment of manufacturing processes. Ecological Indicators, 97, 211–224. Recuperado de https://doi.org/10.1016/j.ecolind.2018.09.062.
Sahu,A., Padhy,R., Das,D., & Gautam,A. (2020). Improving financial and environmental performance through MFCA: A SME case study. Journal of Cleaner Production, 123751, 1-21.
Sajan, M.P., Shalij, P.R., Ramesh, A., Biju Augustine, P., 2017. Lean manufacturing practices in Indian manufacturing SMEs and their effect on sustainability performance. J. Manuf. Technol. Manag. 28, 772e793. https://doi.org/10.1108/ JMTM-12-2016-0188.
Salas,K., Meza,J., Obredor,T., & Mercado,N. (2019). Evaluación de la Cadena de Suministro para Mejorar la Competitividad y Productividad en el Sector Metalmecánico en Barranquilla, Colombia. Información Tecnológica, 30 (2)., 25-32. Recuperado de http://dx.doi.org/10.4067/S0718-07642019000200025.
Salazar, E., & Sarzuri, R. (2015). Algoritmo genético mejorado para la minimización de la tardanza total en un Flowshop flexible con tiempos de preparación dependientes de la secuencia. Revista Chilena de Ingeniería, 23(1), 118-127.
Sangwa,N., & Sangwan,K. (2018). Development of an integrated performance measurement framework for lean organizations. Journal of Manufacturing Technology Management, 29(1).doi:10.1108/JMTM-06-2017-0098.
Sarache, W., Cárdenas, D., Giraldo J., & Parra J. (2007). Procedimiento para evaluar la estrategia de manufactura: aplicaciones en la industria metalmecánica. Pontificia Universidad Javeriana, Facultad de Ciencias Económicas y Administrativas.
Sarache, W., Cárdenas, D., Castrillón, O., Becerra, F., García, A., Giraldo, J., Ibarra, S., Ruiz, S., Tamayo, J., & Zapata, A. (2008). Gestión de la producción: Una aproximación conceptual. Unibiblios, Bogotá.
Sarangi,G., Mishrab, A., Changc,Y., & Taghizadeh,F. (2019). Indian electricity sector, energy security and sustainability: An empirical assessment. Energy Policy, 135, 1-15. Recuperado de https://doi.org/10.1016/j.enpol.2019.110964.
Scassellati,B., Brawer,J., Tsui,K., Gilani,s., Malzkuhn,M., Manini,B., & Stone,K . (2018). Teaching Language to Deaf Infants with a Robot and a Virtual Human. CHI, 21–26, doi: https://doi.org/10.1145/3173574.3174127.
Schaffer, J. (1985). Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the 1st International Conference on Genetic Algorithms, Pittsburgh, PA, USA, 93-100. Recuperado de https://doi.org/10.1016/0066-4138(85)90462-8.
Segersted,E.,& Abrahamsson,L. (2019). Diversity of livelihoods and social sustainability in established mining communities. The Extractive Industries and Society , 6, 610–619. Recuperado de https://doi.org/10.1016/j.exis.2019.03.008.
Selander,J., Rylander,L.,Albin,M., Rosenhall,U.,Lewné,M., & Gustavsson. (2019). Full-time exposure to occupational noise during pregnancy was associated with reduced birth weight in a nationwide cohort study of Swedish women. Science of the Total Environmen, 1137–1143. https://doi.org/10.1016/j.scitotenv.2018.09.212. SENA. (2012). Servicio Nacional de Aprendizaje. Caracterización del sector metalmecánico y área de soldadura. Bogotá.
SENA. (2013). Servicio Nacional de Aprendizaje. Sector metalmecánico: retos de cara al futuro. Obtenido de http://periodico.sena.edu.co/productividad/noticia.php?t=sector-metalmecanico-retos-de-cara-al-futuro&i=868
Sethi, P., Chakrabarti,D., & Hattacharjee,S. (2020). Globalization, financial development and economic growth: Perils on the environmental sustainability of an emerging economy. Journal of Policy Modeling, Recuperado de https://doi.org/10.1016/j.jpolmod.2020.01.007.
Shaheen,A., Spea, S., Farrag,S., & Abido,M. (2018). A review of meta-heuristic algorithms for reactive power planning problem. Ain Shams Engineering Journal , 9, 215–231. Recuperado de http://dx.doi.org/10.1016/j.asej.2015.12.003.
Shahsavari, P. N., & Ghasemishabankareh, B. (2013). A novel hybrid meta-heuristicalgorithm for solving multi objective flexible job shop scheduling. Journal of Manufacturing Systems, 32(4), 771–780.
Shao, X., Liu, W., Liu, Q., & Zhang, C. (2013). Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 67(9-12), 2885–2901. Recuperado de http://doi.org/10.1007/s00170-012-4701-3.
Shen,X., & Yao, X. (2015). Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems. Information Sciences, 298, 198–224. recuperado de http://doi.org/10.1016/j.ins.2014.11.036.
Shen,L., Dauzère,S., Neufeld,D. (2018). Solving the flexible job shop scheduling problem with sequence-dependent setup times. European Journal of Operational Research , 265, 503–516. Recuperado de http://dx.doi.org/10.1016/j.ejor.2017.08.021.
Shen,X., Han,J., & Fu,J. (2017). Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems. Soft Comput, 21, 6531–6554. doi: 10.1007/s00500-016-2245-4.
Shi, J., Jiao, H., & Chen, T. (2012). Multi-objective pareto optimization on flexible job-shop scheduling problem about due punishment. Journal of Mechanical Engineering, 48(12), 184. Recuperado de https://scholar.google.es/scholar?q=Multi-objective+pareto+optimization+on+flexible+jobshop+scheduling+problem+about+due+punishment++&btnG=&hl=en&as_sdt=0%2C5#0.
Shiel,A., Paço,A., & Alves,H. (2020). Generativity, sustainable development and green consumer behaviour. Journal of Cleaner Production , 245,1-9. Recuperado de https://doi.org/10.1016/j.jclepro.2019.118865 .
Shivasankaran, N. (2015). Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling. International Journal of Computational Intelligence Systems, , 8(3), 455-466. Recuperado de http://www.tandfonline.com/doi/abs/10.1080/18756891.2015.1017383.
Shoham,Y. (1997). An Overview of Agent-Oriented Programming. En J. Bradshaw, Software Agents (págs. 271-290). Cambridge, Massachusetts: AAAI Press/The MIT Press.
Shokouhi,E. (2018). Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints. Production & Manufacturing Research, 6 (1), 61-89, Recuperado de https://doi.org/10.1080/21693277.2017.1415173.
Silva,P., Tavares,T., Botelhoa, Oliveira,P., Schaffert,P., Costa,R., & Rodrigues,J. (2020). Intrapopulation recurrent selection in sweet sorghum for improving sugar. Industrial Crops & Products, 143(2), Recuperado de https://doi.org/10.1016/j.indcrop.2019.111910.
Silva,R., Rab,L., Rodrigues,J.,Solic,P., & Carvalho,A. (2020). A preference-based demand response mechanism for energy management in a microgrid. Journal of Cleaner Production, 255 (11),1-14. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120034.
Singh, A. (2013). Minimization of mean tardiness in a flexible job shop. International Journal of Simulation Modelling, 3, 190-204. Recuperado de http://ww.w.ijsimm.com/Full_Papers/Fulltext2013/text12-3_190-204.pdf.
Singh, M., Singh, M., Mahapatra, S., & Jagadev, N. (2016). Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem. International Journal of Advanced Manufacturing Technology, 85(9-12), 2353-2366. doi: 10.1007/s00170-015-8075-1.
Singh, A.P., & Dhadse, K., 2021. Economic evaluation of crop production in the Ganges region under climate change: A sustainable policy framework. J. Clean. Prod. 278, 123413. https://doi.org/10.1016/j.jclepro.2020.123413
Siqueira,C., SouzabS.R. & de Souza,C. (2018). A Multi-objective Variable Neighborhood Search algorithm for solving the Hybrid Flow Shop Problem. Electronic Notes in Discrete Mathematics, 66, 87-94. https://doi.org/10.1016/j.endm.2018.03.012.
Sitcharangsie,S., Ijomah,W., & Wong,T. (2019). Decision makings in key remanufacturing activities to optimise remanufacturing outcomes: A review. Journal of Cleaner Production, 232, 1465- 1481. Recuperado de https://doi.org/10.1016/j.jclepro.2019.05.204.
Srinivas,N & Kalyanmoy,D. (1995). Muiltiobj ective Optimization using nondominated sorting in Genetic Algorithms. Evolutionary Computation , 2(3), 22 1-248.
Staniskienhé, E., & Stankeviciuté, Z. (2018). Social sustainability measurement framework: The case of employee perspective in a CSR-committed organisation. Journal of Cleaner Production, 188, 708-719. Recuperado de https://doi.org/10.1016/j.jclepro.2018.03.269.
Steensels,J., Gallone,B.,Voordeckers,K.,& Verstrepen,K . (2020). Domestication of Industrial Microbes. Current Biology, https://doi.org/10.1016/j.cub.2019.04.025.
Steuer, R., Choo, E. (1983). An interactive weighted tchebychev procedure for multiple objective programming. Mathematical Programming, 26, 326-344. doi:10.1007/BF02591870C.
Suman,G., & Prajapati,D. (2018). Control chart applications in healthcare: a literature review. International Journal of Metrology and Quality Engineering, 9(5),1-21. Recuperado de https://doi.org/10.1051/ijmqe/2018003.
Sun,J., & Xu,L. (2019). Disruption management of multi-objective flexible job shop scheduling problem. Academic Journal Of Manufacturing Engineering, 11(2), 50-56.
Sun,X., Guo,S., & Du,B. (2019). A Hybrid Multi-Objective Evolutionary Algorithm With Heuristic Adjustment Strategies and Variable Neighbor-Hood Search for Flexible Job-Shop Scheduling Problem Considering Flexible Rest Time. IEEE Access, (7), 157003 - 157018.
Suthar,H., & Gadit,J. (2019). Multiobjective optimization of 2DOF controller using Evolutionary and Swarm intelligence enhanced with TOPSIS. Heliyon, 5(9), 2-28, doi: 10.1016/j.heliyon.2019.
Syafaruddin. (2019). Review on Multi-Objectives Optimization Methods in Hybrid Power Generation. Journal of Engineering Science and Technology Review 12 (1) (2019) 143- 152, 12(1)143 -152. doi:10.25103/jestr.121.17.
Ta, V., Griffith,C., Boatfield,C., Wang,X., Civitello,M., Bader, H & Loggarakis,A. (2020). User Experiences of Social Support From Companion Chatbots in Everyday Contexts: Thematic Analysis. Journal Of Medical Internet Research, 22,1-11. Recuperado de http://www.jmir.org/2020/2/e16235/.
Tadros,A., Sharon,M., Chill,N., Dragan,M., Rowell,J., & Hoffman,S. (2018). Emergency department visits for work-related injuries. American Journal of Emergency Medicine , 36, 1455–1458. Recuperado de https://doi.org/10.1016/j.ajem.2018.04.058.
Tamayo, J., Romero,E., Gamero,J., & Martínez,J. (2015). Do Innovation and Cooperation Influence SMEs Competitiveness? Evidence From the Andalusian Metal-Mechanic Sector. Innovar, 25(55), 101-115. recuperado de http://dx.doi.org/10.15446/innovar.v25n55.47226.
Tamerabeta,Y., Adjadja,T ., & Bentrciaa. (2018). Evaluation of the genetic algorithm performance for the optimization of the grand potential in the cluster variation method. Calphad , 61, 157–164. Recuperado de https://doi.org/10.1016/j.calphad.2018.03.007.
Tanabe,K. (2018). Pareto’s 80/20 rule and the Gaussian distribution. Physica, 510, 635–640. Recuperado de https://doi.org/10.1016/j.physa.2018.07.023.
Tang, H., Chen, R., Li,Y., Z., Guo, P., & Du,Y. (2019). Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop. Applied Soft Computing Journal, 78, (21), 176–194. Recuperado https://doi.org/10.1016/j.asoc.2019.02.011.
Tay, J. C., & Ho, N. (2008). Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering, 54(3), 453–473. recuperado de http://doi.org/10.1016/j.cie.2007.08.008.
Tian,N; & Ji,Z. (2015). Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization. Hindawi Publishing Corporation, 7(9),1-11. Recuperado de http://dx.doi.org/10.1155/2015/940592.
Tian,S., Wang,T., Zhang,H., & 4, Wu,H. (2019). An Energy-Efficient Scheduling Approach for Flexible Job Shop Problem in an Internet of Manufacturing Things Environment. IEEEE Access. Special section on green communications on wireless networks, 8(11), 62695- 62704.
Trujillo, M. (2018). Indicador de desempeño ambiental bajo el enfoque GSCM: Validación en empresas manufactureras de la región del eje cafetero. Universidad Nacional de Colombia.
Vargas,Y. (2018). Perfil de salud laboral en Colombia a partir del análisis y caracterización de la enfermedad laboral reportada en el Sistema General de Riesgos Laborales. Periodo 2004 – 2014. Bogotá: Universidad Nacional de Colombia.
Velter,M., Bitzer,V., Bocken,N., & Kemp,R. (2020). Sustainable business model innovation: The role of boundary work for multi-stakeholder alignment. Journal of Cleaner Production , 247, 1-17. Recuperado de https://doi.org/10.1016/j.jclepro.2019.119497.
Venjakob,C., 1,2, Klein,A., Ebeling,A., Tscharntke,T., & Scherbe,C. (2016). Plant diversity increases spatio-temporal niche complementarity in plant-pollinator interactions. Ecology and Evolution, 6(8): 2249–2261.doi: 10.1002/ece3.2026.
Villeneuve,C., Tremblay,D., Riffon,O., Lanmafankpotin,G.,& Bouchard,S. (2017). A Systemic Tool and Process for Sustainability Assessment. Sustainability, 9-19. doi:10.3390/su9101909.
Vital,A., Azaba,A.,& Fazle Bakib,M. (2020). Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility. Journal of Manufacturing Systems , 54, 74–93. Recuperado de https://doi.org/10.1016/j.jmsy.2019.11.010.
Wang, C., Ji, Z.,& Wang,Y. (2017). A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem. Mathematical Problems in Engineering, 1-21. Recuperado de https://doi.org/10.1155/2017/2857564.
Wang, G., Li, C., & Cui, H. (2013a). Simulation Optimization of Multi-Objective Flexible Job Shop Scheduling. Applied Mechanics and Materials, 365(5), 602-605. Recuperado de http://www.scientific.net/AMM.365-366.602.
Wang, S., Liu, C., Pei, D., & Wang, J. (2013b). A novel hybrid election campaign optimisation algorithm for multi-objective flexible job-shop scheduling problem. International Journal of Materials and Structural Integrity, 7(1/2/3), 160. Recuperado de http://doi.org/10.1504/IJMSI.2013.055113.
Wang, W., Fan, L., Xu, X., Zhao, Y., & Zhang, J. (2013c). Multi-objective differential evolution algorithm for flexible Job-Shop batch scheduling problem. Computer Integrated Manufacturing Systems, http://en.cnki.com.cn/Article_en/CJFDTOTAL-JSJJ201310013.htm19(10), 2481–2492. Recuperado de.
Wang, X., Gao, L., Zhang, C., & Shao, X. (2010a). A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. International Journal of Advanced Manufacturing Technology, 51, 757-767. doi: 10.1007/s00170-010-2642-2. doi: 10.1007/s00170-013-4923-z.
Wang, X., Li, W., & Zhang, Y. (2013d). An improved multi–objective genetic algorithm for fuzzy flexible job–shop scheduling problem. International Journal of Computer, 47(2/3), 280-288.
Wang, Y., Feng, Y., Tan, J., Gao, Y. (2011). Multi-objective optimization method of flexible job-shop lot-splitting scheduling. Journal of Zhejiang University (Engineering Science), 45(4), 719-726.
Wang,J., Yang,J.,Zhang,Y., Ren,S & Liu,Y. (2020). Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods. Journal of Cleaner Production, 247, Recuperado de https://doi.org/10.1016/j.jclepro.2019.119093.
Wang,J., Zhang,B., Li, Lin., Bai, D., & Feng, Y. (2020a). Due-window assignment scheduling problems with position-dependent weights on a single machine, Engineering Optimization, 52:2, 185-193, DOI: 10.1080/0305215X.2019.1577411
Wang,L., Zhou, G., Xu,Y., & Min,L. (2012). An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling. Int J Adv Manuf Technol , 11(60), 1111–1123.doi: 10.1007/s00170-011-3665-z.
Wang,Y.,& Shawb,D. (2018). The complexity of high-density neighbourhood development in China: Intensification, deregulation and social sustainability challenges. Sustainable Cities and Society, 43, 578–586. Recuperado de https://doi.org/10.1016/j.scs.2018.08.024.
Wassenhove,L.,& Gelders,L. (1980). Solving a bicriterion scheduling problem. European Journal of Operational Research, 4 (1), 42-48. Recuperado de https://doi.org/10.1016/0377-2217(80)90038-7Get rights and content.
Wolowski,L., Carvalheiro,L.,& Freitas,L. (2017). Influence of plant–pollinator interactions on the assembly of plant and hummingbird communities. Journal of Ecology 2017, 105, 332–344. doi: 10.1111/1365-2745.12684.
Woodcraft, S. (2012). Social sustainability and new communities: Moving from concept to practice in the UK. Procedia-Social and Behavioral Sciences, 68, 29–42. Recuperado de http://doi.org/10.1016/j.sbspro.2012.12.204.
Wouters,M.,& Stecher,J. (2017). Development of real-time product cost measurement: A case study in a medium-sized manufacturing company. Int. J. Production Economics, 183, 235–244. Recuperado de http://dx.doi.org/10.1016/j.ijpe.2016.10.018.
Wu,R., Li,Y., Guo,S., & Li,X. (2018). An Efficient Meta-Heuristic for Multi-Objective Flexible Job Shop Inverse Scheduling Problem. IEEE Access, 59515- 59527.
Wu,X.,& Sun,Y. (2018). A green scheduling algorithm for flexible job shop with energy-saving. Journal of Cleaner Production, 3249- 3264. Recuperado de https://doi.org/10.1016/j.jclepro.2017.10.342.
Wu,W., Shen, X & Lib,C. (2019). The flexible job-shop scheduling problem considering deterioration effect and energy consumption simultaneously. Computers & Industrial Engineering , 135, 1004–1024. Recuperado de https://doi.org/10.1016/j.cie.2019.06.048.
Xia,W & Zu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48, 409–425. doi:10.1016/j.cie.2005.01.018.
Xie,J., Gao,L., Peng,L.,Li,X., & Li,H. (2019). Review on flexible job shop scheduling. IET Collaborative Intelligent Manufacturing, 1(3), 67-77. doi: 10.1049/iet-cim.2018.0009.
Xing, L., Chen, Y., & Yang, K . (2008). Double Layer ACO Algorithm for the Multi-Objective FJSSP. New Generation Computing, 26(4), 313–327. Recuperado de http://doi.org/10.1007/s00354-008-0048-6.
Xing,L., Chen,Y., & Yang,K. (2009a). Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling. Applied Soft Computing, 9(11), 362–376.
Xing,L., Chen,Y.,& Yang,K. (2009b). An efficient search method for multi-objective flexible job shop. J Intell Manuf, 20, 283-293. doi: 10.1007/s10845-008-0216-z.
Xiong, J., Tan, X., Yang, K., Xing, L., & Chen, Y. (2012). A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems. . Mathematical problems in engineering, 12(33), 12-27p. doi:10.1155/2012/478981.
Xiong, J., Xing, L., & Chen, Y. (2013). Robust scheduling for Multi-objective Flexible Job Shop problems with random machine breakdowns. International Journal of Production Economics, 141(1), 112–126. Recuperado de http://www.sciencedirect.com/science/article/pii/S0925527310001739.
Yang, Y., Huang, M.,Wang, Z.,Zhu, Q. (2018). Solving Bi-objective FJSP Using Limited Stable Matching Strategy. China Mechanical Engineering, 29(14), 1743-1750.
Yang,Y., Zeng,Z., Wang,R & Sun,X. (2016). Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times. journal One, 11(12), doi:10.1371/journal.pone.0167427.
Ye, J., & Ma, H. (2015). Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem. Mathematical Problems in Engineering, 4(11),1-9. Recuperado de http://www.hindawi.com/journals/mpe/2015/725460/abs/.
Yi,J., Xing,L., Wang, G.,Dong,J., Vasilakos,A., & Alavi, A. (2020). Behavior of crossover operators in NSGA-III for large-scale optimization problems. Information Sciences , 509, 470–487. Recuperado de https://doi.org/10.1016/j.ins.2018.10.005.
Yilmaz, O .,& Durmusolgu,C. (2018). A performance comparison and evaluation of metaheuristics for a batch scheduling problem in a multi-hybrid cell manufacturing system with skilled workforce assignment. Journal of Industrial and Management Optimization, 14(1), 1219-1249.
Yin,L., Li,X., Gao,L., Lu,C.,& Zhang,Z. (2017). A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem. Sustainable Computing: Informatics and Systems, 13, 15–30. Recuperado de http://dx.doi.org/10.1016/j.suscom.2016.11.002.
Yu, W., & Shi,X. (2019). An intelligence evaluation method of the environmental impact for the cutting process. Journal of Cleaner Production , 227, 229- 236. Recuperado de https://doi.org/10.1016/j.jclepro.2019.03.336.
Yuan, K., Zhu, J., Ju,Q., & Wang, Y. . (2006). Integrated operator genetic algorithm for solving multi-objective flexible job-shop scheduling. Transactions of Nanjing University of Aeronautics and Astronautics, 278-282.
Yuan, Y., & Xu, H. (2015). Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms. Transactions on Automation Science and Engineering, 12(1), 336–353. Recuperado de http://doi.org/10.1109/TASE.2013.2274517.
Yuan,J., Chen,K., Li,W., Ji,C., Wang,Z., & Skibniewski,M. (2018). Social network analysis for social risks of construction projects in high-density urban areas in China. Journal of Cleaner Production, 198, 940- 961. Recuperado de https://doi.org/10.1016/j.jclepro.2018.07.109.
Zaman,F., Elsayed,S., Sarker,R., Essam, D., & Coello,C. (2021). An evolutionary approach for resource constrained project scheduling with uncertain changes. Computers and Operations Research 125 (2021) 105104, 125, 105104 - 105104.
Zapata Gomez, A. (2014). La gestión ambiental en el sector empresarial, una visión bajo el enfoque Empresa-Entorno como estrategia de competitividad. Bpgotá: Universidad Nacional de Colombia.
Zeleny, M. (1973). Compromise Programming. in Multiple Criteria Decision Making. Columbia, USA: University of South Carolina Press.
Zeng, Q., Yang,Y., Shen, L., Zhang, J. (2011). Multiobjective optimization for batch production FJSP based on just in time delivery. Computer Integrated Manufacturing Systems, 17(8),1780-1789.
Zhang, C.,Dong, X., Wang, X.,Li,X., & Liu,Q. (2010). Improved NSGA-II for the multi-objective flexible job-shop scheduling problem. Journal of Mechanical Engineering, 46(11), 156-16.
Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial Engineering, 56(4),1309–1318. Recuperado de http://doi.org/10.1016/j.cie.2008.07.0.
Zhang,H., Ge,H., Pan,R & Wu,Y. (2018a). Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout. Algorithms, 11, 210. doi:10.3390/a11120210.
Zhang,K., Shen,C., He,J., & Yen,G. (2020). Knee based multimodal multi-objective evolutionary algorithm for decision making. Information Sciences, 544, 1- 17.
Zhang,L., Tang,Q., Wu,Z., & Wang,F. (2017a). Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops. Energy, 138, 210-227. Recuperado de http://dx.doi.org/10.1016/j.energy.2017.07.005.
Zhang,Y., Wang,J.,& Liu,Y. (2017b). Game theory based real-time multi-objective flexible job shop scheduling considering environmental impact. Journal of Cleaner Production, 16, 665-679. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2017.08.068.
Zhang,Z., Wu,L., Peng,T., & Shun,J . (2018b). An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment. Sustainability, 11(179), 1-21. doi:10.3390/su11010179.
Zheng, Y., Li, Y., & Lei, D. (2012). Multi-objective swarm-based neighborhood search for fuzzy flexible job shop scheduling. International Journal of Advanced Manufacturing Technology, 60 (9), 1063-1069. DOI: 10.1007/s00170-011-3646-2
Zhenwen,D. (2019). Application of multi-objective memetic algorithm in multi-objective flexible job-shop scheduling problem. Academic Journal of Manufacturing Engineering , 17(3), 24-28.
Zhou,B., & Liao,X. (2020). Particle filter and Levy flight-based decomposed multi-objective evolution hybridized particle swarm for flexible job shop greening scheduling with crane transportation. Applied Soft Computing Journal, 91, 1-18.
Zhou,X., & Peng,T. (2020). Application of multi-sensor fuzzy information fusion algorithm in industrial. Safety Science , 122,1-5. Recuperado de https://doi.org/10.1016/j.ssci.2019.104531.
Zhou,B., Liu,B., Yang, D., Cao,D., & Littler,T. (2020). Multi-objective optimal operation of coastal hydro-electrical energy system with seawater reverse osmosis desalination based on constrained NSGA-III. Energy Conversion and Management, 207, 1- 15. Recuperado de https://doi.org/10.1016/j.enconman.2020.112533.
Zhu, C., Qiu, W., Zhu, M., & Chen, G. (2016). Multi-objective flexible job shops robust scheduling problem under stochastic processing times. China Mechanical Engineering Magazine Office, 27, 1667-1672. doi: 10.3969/j.issn.1004-132X.2016.12.019.
Zhu, G., & Xu, W. (2019). Multi-objective flexible job shop scheduling method for machine tool component production line considering energy consumption and quality. Control and Decision, 34(2), 252-260.
Zhu,W. (2017). Multi-objective dynamic scheduling algorithm for flexible job-shop problem based on rule orientation. System Engineering Theory and Practice, 37(1), 2690-2699.
Zhu,Z., & Zhou,C. (2020). An efficient evolutionary grey wolf optimizer for multi-objective flexible job shop scheduling problem with hierarchical job precedence constraints. Computers & Industrial Engineering, 140, 1-16.
Zhu,H.,Hea,B., & Lib,H. (2017). Modified Bat Algorithm for the Multi-Objective Flexible Job Shop Scheduling Problem. International Lournal of Performability Endineering, 13 (7), 999-1012, doi: 10.23940/ijpe.17.07.p1.9991012.
Zitzler, E., Laumanns,M & Thiele,L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm. ETH Zentrum. Research Collection, 1-22. Recuperado de https://doi.org/10.3929/ethz-a-004284029.
Zitzler,C., Laumanns,M., & Thiele,L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Computer Engineering and Networks Laboratory (TIK), 1-23.
Zitzler,E., & Thiele,L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257-271. doi: doi: 10.1109/4235.797969.
Zitzler,E., Deb,K., & Thiele,L. (2000). Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2): 173-195, 8(2), 173-195.
Zou,S., Yaod, X., Zhongd,C., Zhaod, T.,& Huanga,H. (2019). Effectiveness of recurrent selection in Akebia trifoliata (Lardizabalaceae) breeding. Scientia Horticulturae 246 (2019) 79–85, 246, 79–85. Recuperado de https://doi.org/10.1016/j.scienta.2018.10.060.
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spelling Castrillón Gómez, Omar Danilodf8b2e171ca5874bdef91d9df638b01dRuiz Herrera, Santiago7da5b8244db11085d614046c4e906ee2Coca Ortegón, Germán Augusto5cb2ab727ac324f61dd9afbee4493dd92021-05-04T19:37:53Z2021-05-04T19:37:53Z2021-03-12https://repositorio.unal.edu.co/handle/unal/79474Universidad Nacional de ColombiaRepositorio Universidad Nacionalhttps://repositorio.unal.edu.co/anexos, figuras, tablas.En la presente Tesis, se examinó como objeto de estudio, la configuración productiva correspondiente al “taller de trabajo flexible”, en el sector industrial metalmecánico. De este modo, la configuración en cuestión se analizó, por medio de la utilización de los campos teóricos que atañen a la secuenciación, a la sostenibilidad y a la programación multiobjetivo. La secuenciación, permite estimar el tiempo de proceso en los diversos tipos de sistemas de manufactura. Asimismo, el concepto inherente a la sostenibilidad, se ha incorporado en la gestión organizacional, con el propósito de lograr el desempeño equilibrado de sus dimensiones social, ambiental y económica. La aplicación de la sostenibilidad en la secuenciación de la configuración evaluada, se ha orientado fundamentalmente hacia el análisis de dos de sus dimensiones, la económica y la ambiental. Al respecto, se indica que el estudio de la primera de estas dimensiones, se ha enfocado en la medición de la eficiencia operativa; mientras que la segunda se ha centrado en el examen específico del consumo de energía en manufactura. De acuerdo con lo anterior, se determinaron los siguientes aspectos en relación al estado de la programación de producción de la configuración “taller de trabajo flexible”, bajo una perspectiva de sostenibilidad: no se han valorado en su conjunto las tres dimensiones de la sostenibilidad; no se han analizado diversas variables relevantes de sostenibilidad en la dimensión ambiental; y no se ha profundizado la evaluación de la dimensión social. A partir de lo expuesto, se planteó la necesidad de minimizar a nivel multiobjetivo por cada dimensión de la sostenibilidad, las siguientes variables: a nivel social, postura o nivel de intensidad sonora como causas de enfermedad laboral o la accidentalidad laboral o sus causas; a nivel ambiental, emisiones equivalentes de dióxido de carbono en manufactura y en sus equipos de apoyo, consumo de agua, desperdicio de metales o de solución de cromo; y a nivel económico, tiempo de proceso, tardanza total o costo de recurso utilizado. Con el fin de solucionar el problema multiobjetivo, se diseñaron tres métodos basados en algoritmos genéticos. Estos métodos se aplicaron a la información tomada de una compañía. Es así como, se observó que el “método uno” superó respectivamente a los métodos dos y tres, en cuanto al desempeño del indicador C-Metric. A su vez, también se procesó en el programa estructurado para la ejecución del ya nombrado “método uno”, aquella información registrada en dos referentes documentados en la literatura. En este sentido, se comprobó que el método en cuestión superó el rendimiento de cada uno de los métodos desarrollados por tales referentes de cotejo, en lo concerniente al indicador previamente citado, C-Metric. Por último, se determinan entre otras, para la configuración productiva examinada bajo criterios de sostenibilidad, las siguientes líneas futuras de investigación: diseño de un modelo que priorice el análisis de ciertas situaciones de incertidumbre, considerando para este efecto, la perspectiva de equilibrio de las tres dimensiones de la sostenibilidad e, igualmente, también se pretende establecer, la integración de conceptos como: dinámica de sistemas; y formulación y evaluación de proyectos, al estudio de la sostenibilidad.In the present thesis, the object of study was the productive configuration of “Flexible Job Shop” in the metal mechanics industrial sector. The configuration in question was analyzed, then, by way of theoretical fields that concern scheduling, sustainability, and multi-objective programming. Schedulling permits the estimation of makespan time in various types of manufacturing systems. Similarly, the inherent concept of sustainability has been incorporated into organizational management, so as to achieve balanced performance in the social, environmental, and economic dimensions. The application of sustainability to schedule the evaluated configuration was fundamentally oriented toward the analysis of two of its dimensions: the economic and environmental. The study of the first of these dimensions is focused on the measurement of operational efficiency, while the second concentrates on the specific examination of energy consumption in manufacturing. In accordance with the above, the following aspects were determined, as they relate to the state of “Flexible Job Shop” configuration production programming, from the sustainability perspective: the three dimensions of sustainability have not been evaluated simultaneously, diverse variables relevant to sustainability have not been analyzed from the environmental dimension, and the evaluation of the social dimension has not been explored in depth. Based upon the above, the need to minimize the multi-objective level, in each sustainability dimension, was proposed, for the following variables: on the social level, the posture or level of sound intensity, as causes of work illness or accidents, or their causes, on the environmental level, equivalent emissions of carbon dioxide in manufacturing and its support devices, water consumption, metal or chrome solution waste, and on the economic level, makespan time, total tardiness, or cost of the resource utilized. In order to resolve the multi-objective problem, three methods were designed, based on genetic algorithms. These methods applied the information taken from a primary company. Thus, it was observed that “method one” surpassed methods two and three, in terms of C-Metric indicator performance. Further, that information registered for the two references, as documented in the literature, was processed by the program that had been structured for the implementation of “method one”. It was proven, by these means, that the method in question surpassed, the performance of each of the methods developed by said references, by comparison, in that concerning the C-Metric indicator. Lastly, among other things, the following future lines of research were determined, for the productive configuration examined with sustainability criteria: the design of a model that prioritizes the analysis of various uncertainty situations, considering the equilibrium perspective of the three sustainability dimensions, and similarly, one could also seek to integrate concepts such as: systems dynamic; and projects formulation and evaluation, for sustainability studying.DoctoradoDirección de Producción y Operaciones303 p.application/pdfspaUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Industria y OrganizacionesDepartamento de Ingeniería IndustrialFacultad de Ingeniería y ArquitecturaManizalesUniversidad Nacional de Colombia - Sede Manizaleshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2670 - Manufacturaindustria metalmecánicasector industrialmetalworking industryindustrial sectoralgoritmos genéticos multiobjetivoconfiguración de “taller de trabajo flexible”sector metalmecánicoemisiones dióxido de carbonoposturatardanza totalSustainabilitymulti-objective genetic algorithms“Flexible Job shop” configurationmetal mechanic sectorcarbon dioxide emissionsposturetotal tardinessMetodología multiobjetivo para la programación de producción de los sistemas de manufactura "Job Shop Flexible" del sector metalmecánico, bajo un enfoque de sosteniblidadMultiobjective methodology for "flexible job shop" manufacturing systems production scheduling of metalmechanic sector, under a sustainability approachTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAbdelmaguid, T. (2015). A neighborhood search function for flexible job shop scheduling with separable sequence-dependent setup times. Applied Mathematics and Computation, 260, 188–203. Recuperado de http://doi.org/10.1016/j.amc.2015.03.059.Adam,S.,& Bruno, R. (2015). ReviewPareto’s 80/20 law and social differentiation: A social entropy perspective. Public Relations Review, 41, 178–186. Recuperado de http://dx.doi.org/10.1016/j.pubrev.2014.11.006.Ahmad,S., Wong,K., Tseng,M., & Wong,W. (2018). Sustainable product design and development: A review of tools, applications and research prospects. Resources, Conservation & Recycling, 132, 49–61.Ahmadi, E., Zandieh, M., Farrokh, M., & Emami, S. (2016). A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms. Computers and Operations Research, 73, 56-66. doi:10.1016/j.cor.2016.03.009.Ahmed,W., Ashraf,M., Khan,S., Sarpong,S., Arhin,F., Sarpong., & Najmi,A. (2020). Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm’s sustainable performance. Operations Management Research , 13, 4–21.Akbar,M., & Irohara,T. (2018). Scheduling for sustainable manufacturing: A review. Journal of Cleaner Production 205 (2018) 866e883, 205, 866- 883. Recuperado de https://doi.org/10.1016/j.jclepro.2018.09.100.Adewuyi, O.B., Shigenobu, R., Senjyu, T., Lotfy, M.E., & Howlader, A.M. (2019). Multiobjective mix generation planning considering utility-scale solar PV system and voltage stability: Nigerian case study. Electr. Power Syst. Res. 168, 269–282. https://doi.org/10.1016/j.epsr.2018.12.010Akgun, I.,& Erdal,H. (2019). Solving an ammunition distribution network design problem using multi-objective mathematical modeling, combined AHP-TOPSIS, and GIS. Computers & Industrial Engineering, 129, 512-528. Obtenido de https://doi.org/10.1016/j.cie.2019.02.004.Allahverdi, A. (2015). The third comprehensive survey on scheduling problems with setup times/costs. . European Journal of Operational Research, 246(2), 345-378.Allahverdi, A., Ng, C. T., Cheng, T. E., & Kovalyov, M. Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, 187(3), 985-1032.Almahmoud,E., & Doloi, H. (2015). Assessment of social sustainability in construction projects using social network analysis. Facilities, 33, 152-176. Recuperado de https://doi.org/10.1108/F-05-2013-0042.Azzouz, A., Chaabani, A., Ennigrou, M., & Said, L. (2020). Handling Sequence-dependent Setup Time Flexible Job Shop Problem with Learning and Deterioration Considerations using Evolutionary Bi-level Optimization. Applied Artificial Intelligence, 34:6, 433-455, doi: 10.1080/08839514.2020.1723871.Amin,N., Noah,R.,Quek, J., Oxley ,J., & Rusli,B. (2020). Perceived physical demands in relation to work-related musculoskeletal disorders among nurses. Materials Today: Proceedings , 7(21),1-6. Recuperado de https://doi.org/10.1016/j.matpr.2020.01.196.Amiri, F., Shirazi, B., & Tajdin, A. (2019). Multi-objective simulation optimization for uncertain resource assignment and job sequence in automated flexible job shop. Applied Soft Computing Journal 75 (2019) 190–202, 75, 190–202. Recuperado de https://doi.org/10.1016/j.asoc.2018.11.015.Amini, M., & Bienstock, C. (2014). Corporate sustainability: an integrative definition and framework to evaluate corporate practice and guide academic research. Journal of Cleaner Production, 76, 12–19. Recuperado de http://doi.org/10.1016/j.jclepro.2014.02.016.An, Y., Chen, X., Zhang, J., & Li,L. (2020). A hybrid multi-objective evolutionary algorithm to integrate optimization of the production scheduling and imperfect cutting tool maintenance considering total energy consumption. Journal of Cleaner Production, 1-28. Recuperado de https://doi.org/10.1016/j.jclepro.2020.121540.Ardjmand,E., Sanei,O., & Youssef,O. (2019). Using list-based simulated annealing and genetic algorithm for order batching and picker routing in put wall based picking systems. Applied Soft Computing Journal , 75, 106–119. Recuperado de https://doi.org/10.1016/j.asoc.2018.11.019.Ari,E., Karatepe,O., Rezapouraghdam,H., & Avci,V. (2020). A Conceptual Model for Green Human Resource Management: Indicators, Diferential Pathways,and Multiple Pro-Environmental Outcomes. Sustainability doi:10.3390/su12177089 www.mdpi, 12, 1-18. doi:10.3390/su12177089 www.mdpi.Arcidiacono, G., Costantino, N., Yang, K., 2016. The AMSE lean six sigma governance model. Int. J. Lean Six Sigma 7, 233e266. https://doi.org/10.1108/IJLSS-06-2015- 0026Armindo,J., Fonseca,J., Abreu,I & Toldy,T. (2019). Perceived importance of sustainability dimensions in the Portuguese metal industry. International Journal of Sustainable Development & World Ecology, 26:2, 154-165. doi: 10.1080/13504509.2018.1508524.Aryanny,E.,& Iriani. (2019). Analysis of Quality Management by Implementing Total Quality Management Based on Deming Prize. International Conference on Science and Technology , 2-7. doi:10.1088/1742-6596/1569/3/032015.Aschauer,A., Roetzer.,A., Steinboeck,A., & Kugi. (2017). An Efficient Algorithm for Scheduling a Flexible Job Shop with Blocking and No-Wait Constraints. IFAC PapersOnLine, 50 (1), 12490–12495.Autuori,J., Hnaien,F.,& Yalaoui. (2016). Three metaheuristics improved by a mapping method. IFAC (International Federation of Automatic Control), 49-12, 1472–1477. doi: 10.1016/j.ifacol.2016.07.779.Bahar, N., Noor, Z., Aris, A., & Kamaruzaman, N. (2020). An indicator framework approach on manufacturing water assessment towards sustainable water demand management. Journal of Environmental Treatment Techniques, 8(3), 875-883.Balaraju, G., Venkatesh, S., & Reddy, B. (2014). Multi-objective flexible job shop scheduling using hybrid differential evolution algorithm. International Journal of internet Manufacturing and Services, 3(3), 226-243.Bamgbade,J., Kamaruddeen,M., & Nawi,M. (2017). Malaysian construction firms social sustainability via organizational innovativeness and government support: The mediating role of market culture. Journal of Cleaner Production, 154, 114-124. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2017.03.187.Banco Mundial. (2016). Datos sobre las cuentas nacionales del Banco Mundial. Obtenido de http://datos.bancomundial.org/indicador/NY.GDP.PCAP.CDBanerjee,S.,& . (2019). The occupational illness of slum dwellers across industries: A case study in West Bengal. International Journal of Industrial Ergonomics , 73 (10), 1-9. Recuperado de https://doi.org/10.1016/j.ergon.2019.102834.Belhadi,A.,Sha’ri,Y., Touriki,F & El Fezazi,S. (2018). Lean production in SMEs: literature review and reflection on future challenges. Journal of Industrial and Production Engineering, 35 (6), 368–382.Bihari M., & Kane P. (2021). Evaluation and Improvement of Makespan Time of Flexible Job Shop Problem Using Various Dispatching Rules—A Case Study. In: Kalamkar V., Monkova K. (eds). Mechanical Engineering, 11 (9), 21-31. Recuperado de https://doi-org.ezproxy.unal.edu.co/10.1007/978-981-15-3639-7_73.Bi,X.,& Wang,C. (2018). A niche-elimination operation based NSGA-III algorithm for many-objective optimization. Appl Intell, 48,118–141. doi:10.1007/s10489-017-0958-4.Bissoli, D.,Altoe,W., Mauri,G & Amaral,A. (2018). A simulated annealing metaheuristic for the bi-objective flexible job shop scheduling problem,. International Conference on Research in Intelligent and Computing in Engineering (RICE), San Salvador, 1-6, doi: 10.1109/RICE.2018.8627907.Bissoli,D., Zufferey,N., & Amaral,A. (2019). Lexicographic optimization-based clustering search metaheuristic for the multiobjective flexible job shop scheduling problem. International Transactions in Operational Research, 1–26.Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 41 (3), 157-183. Recuperado dehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-34250084648&doi=10.1007%2fBF02023073&partnerID=40&md5=e855d121de089837fab8733d864abc60.Brit, J. (2015). Stochastic Goal Programming and a Metaheuristic for Scheduling of Operating Rooms. Canadá: Universidad de Windsor .Bukchin, Y., Hanany, E., 2020. Decentralization cost in two-machine job-shop scheduling with minimum flow-time objective. IISE Trans. 52(12), 1386–1402. https://doi.org/10.1080/24725854.2020.1730528Burki,u.,Ersoy,p., & Dahlstrom,B. (2018). Achieving triple bottom line performance in manufacturer-customer supply chains: Evidence from an emerging economy. Journal of Cleaner Production , 197, 1307-1316. Recuperado de https://doi.org/10.1016/j.jclepro.2018.06.236.Büyüközkan, B., Kayakutlu,G.,& Karakadılar,I. (2015). Assessment of lean manufacturing effect on business performance using Assessment of lean manufacturing effect on business performance using. Expert Systems with Applications, 42, 6539–6551. Recuperado de http://dx.doi.org/10.1016/j.eswa.2015.04.016.Cai,Y., Wang,F., Tan,G., Hu,Z., Wang,Y., Lun,W., Wu,W., Liu,Y & Zhou,R. (2019). Hybridization of Bornean Melastoma: implications for conservation of endemic plants in Southeast Asia. Botany Letters, 166 (2), 117–124. Recuperado de https://doi.org/10.1080/23818107.2019.1585284.Caldeira R.H., & Gnanavelbabu A. (2021). Solving the Flexible Job Shop Scheduling Problem Using a Hybrid Artificial Bee Colony Algorithm. En S. N. Vijayan S., Trends in Manufacturing and Engineering Management. Springer.Caldwell,J., Caldwell,L., Thompson,L., & Lieberman,H. (2019). Fatigue and its management in the workplace. Neuroscience and Biobehavioral Reviews, 96, 272–289.Calleja, G., & Pastor, R. (2013). A dispatching algorithm for flexible job-shop scheduling with transfer batches: an industrial application. Production Planning & Control. Obtenido de http://www.tandfonline.com/doi/abs/10.1080/09537287.2013.782846Canales,A., & Campos,A. (2016). Modelamiento Predictivo de la Pérdida Auditiva Laboral, Relacionada con el Tratamiento de Absorción Acústica en una Industria Metal-Mecánica en Chile. Ciencia & trabajo, 11(9), 21-29.Carpio de los Pinos,A., & González,M . (2020). Development of the Protocol of the Occupational Risk Assessment Method for Construction Works: Level of Preventive Action. International Journal of Environmental Research and Public Health, 17, 6369, 1-33. doi:10.3390/ijerph17176369.Castro, V., Herrera, R., & Villalobos, M. (2020). Development of a web software to generate management plans of software risks. Revista Información Tecnológica, 31(3), 135-148. Recuperado de http://dx.doi.org/10.4067/S0718-07642020000300135.CCMPC. (2019). Cámara de Comercio de Manizales por Caldas. Informe económico anual - Manizales y Caldas. Manizales.CCMPC. (2014). Camara de Comercio de Manizales por Caldas.Caracterización sector metalmecánico de la ciudad de Manizales. Manizales.CCS. (2019). Consejo Colombiano de Seguridad. Cómo le fue a Colombia en accidentalidad, enfermedad y muerte laboral en 2018. Obtenido de https://ccs.org.co/como-le-fue-a-colombia-en-accidentalidad-enfermedad-y-muerte-laboral-en-2018/CESEDEN. (2018). Centro Superior de la Defensa Nacional. La Inteligencia artificial aplicada a la defensa.Charnes,A., Cooper,W.,& Ferguson,R. (1995). Optimal Estimation of Executive Compensation by Linear Programming. Management Science, 138-151, Recuperado de https://doi.org/10.1287/mnsc.1.2.138.Charniak,E & McDermott. (1985). Introduction to Artificial Intelligence. Reading, Mass. Addison-Wesley.Chen, H., Jiang, Z., Zuo, L., & Zhang, Y. (2015). Multi-objective flexible job-shop scheduling problem based on NSGA-Ⅱ with close relative variation. Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, 46(4), 344–350. Recuperado de http://doi.org/10.6041/j.issn.1000-1298.2015.04.051.Chen, Y., Wang, J., & Yang, Z. (2015a). The human factors/ergonomics studies for respirators: a review and future work. International Journal of Clothing Science and Technology, 27(5), 652–676. Recuperado de http://doi.org/10.1108/IJCST-06-2014-0077.Chen,G., Johnson,K., Morales,E., Ibáñez,A., & Lin,T. (2018). A high-Oil castor cultivar developed through recurrent selection. Industrial Crops & Products, 111, 8–10. Recuperado de http://dx.doi.org/10.1016/j.indcrop.2017.09.064.Chen, X., Liang, Y., Sterna, M., Wang, W., & Błażewicz, J. (2020a). Fully polynomial time approximation scheme to maximize early work on parallel machines with common due date. Eur. J. Oper. Res. 284, 67–74. https://doi.org/10.1016/j.ejor.2019.12.003Chen,X., An,Y., Zhang,Z., & Li,Y. (2020). An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals. Journal of Manufacturing Systems , 54, 227–241.Cheng,C., , Zhijun,C., & Chenghua,S. (2003). A novel design method: a genetic algorithm applied to an erbium-doped fiber amplifier. Optics Communications, 227 (11), 371–382.doi:10.1016/j.optcom.2003.09.055.Chetan., Ghosh, S., & Venkateswara Rao, P. (2015). Application of sustainable techniques in metal cutting for enhanced machinability: a review. Journal of Cleaner Production, 100, 17–34. Recuperado de http://doi.org/10.1016/j.jclepro.2015.03.039.Chiang, T., & Lin, H. (2011). Flexible job shop scheduling using a multiobjective memetic algorithm. . Proceedings of the 7th International Conference on Intelligent Computing (ICIC). Zhengzhou, China, 49-56. Recuperado de http://link.springer.com/chapter/10.1007.Chiang, T., & Lin, H. (2013). A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. International Journal of Production Economics, 141(1), 87–98. Recuperado de http://doi.org/10.1016/j.ijpe.2012.03.034.Chunguang,B., Sarpong,S.,& Sarkis,J. (2017). An implementation path for green information technology systems in the Ghanaian mining industry. Journal of Cleaner Production, 164, 1105-1123.Coca, G. A., Castrillón, O. D., & Ruiz, S. (2015a). Design of a Novel Methodology for Multiobjective Anlysis in a Job Shop Manufacturing System, Based on Comparison of Subpopulations. In Proceedings of the 27th European Conference on Operational Research. July 12-15, 2015. Glasgow, Scotland: University of Strathclyde, 224. Recuperado de https://www.euro- online.org/media_site/reports/EURO27_AB.pdf.Coca, G., Castrillón, O., & Ruiz, S. (2015b). El sonido y la iluminación en la programación multiobjetivo de un sistema de manufactura tipo “Job Shop”. Memorias del décimo primer congreso de Investigación Operativa – ÓPTIMA 2015. Octubre 18 al 21 de 2015. Ediciones Universitarias : Antofagasta - Chile.Coca, G. A., Castrillón, O. D., & Ruiz, S. (2016). Programación de un Sistema de Fabricación tipo “job shop” bajo un Enfoque de Sostenibilidad. Revista Información Tecnológica, 27(6), 31-52. doi: 10.4067/S0718-07642016000600005.Coca, G. A., Castrillón, O. D., & Ruiz, S. (2018). Los grupos de interés en la programación de producción de sistemas de manufactura “Job Shop” utilizando un algoritmo genético multiobjetivo. Proceeding of the XIX Latin Iberoamerican Conference on Operations Research - CLAIO 2018. September 24-27, 2018. Lima, Perú: @ Sociedad Peruana de Investigación Operativa (SOPIOS), 55, Recuperado de http://www.sopios.org.pe/static/claio/proceeding.pdf.Coca, G. A., Castrillón, O. D., & Ruiz, S. (2019a). Los grupos de interés en la programación de producción de un sistema de manufactura “Job Shop” . Revista EIA, 16(32), 65-84. Recuperado de https://doi.org/10.24050/reia.v16i32.1236.Coca, G. A., Castrillón, O. D., Ruiz, S., Mateo-Sanz, J., & Jiménez, L. (2019b). Sustainable evaluation of environmental and occupational risks scheduling flexible job shop manufacturing systems. Journal of Cleaner Production, 209, 146-168. Recuperado de https://doi.org/10.1016/j.jclepro.2018.10.193.Coello, A., Christiansen & Aguirre,H. (1995). Multiobjective design optimization of counterweight balancing of a robot arm using genetic algorithms. Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence. Herndon, VA, USA , 20-23.doi: 10.1109/TAI.1995.479374.Comisión Europea. (2016). Acuerdo de París. Obtenido de https://ec.europa.eu/clima/policies/international/negotiations/paris_esConfecámaras. (2017). Contribución de las iniciativas clúster al desarrollo regional. Bogotá.Conpes 3918. (2018). Estrategia para la implementación de los objetivos de desarrollo sostenible (ods) en Colombia. Bogotá: Consejo Nacional de Política Económica Y Social.Departamento Nacional De Planeación.Cooper,M. (2019). The efficacy of industrial safety science constructs for addressing serious injuries & fatalities (SIFs). Safety Science , 120, 164–178. Recuperado de https://doi.org/10.1016/j.ssci.2019.06.038.Corentin, L. (2019). Integrating waste minimization concerns in operations scheduling. These de Doctorat de L’Universite de Lyon (Ecole Doctorale Informatique et Mathématiques de Lyon). Recuperado de : http://theses.insa-lyon.fr/publication/2019LYSEI111/these.pdf.Cuayal,J.,& Romero,L. (2016). Documentación del sistema de gestión ambiental en el marco de la NTC-ISO 14001 para la E.S.E hospital universitario San Jorge de la ciudad de Pereira. Pereira, Risaralda: Universidad Tecnológica de Pereira.Cui,Z., Chang,Y., Zhang,J., Cai,X., & Zhang,W. (2019). Improved NSGA-III with selection-and-elimination operator. Swarm and Evolutionary Computation , 49, 23–33. Recuperado de https://doi.org/10.1016/j.swevo.2019.05.011.D´Atlas. (2018). Manizales Met. Obtenido de http://datlascolombia.bancoldex.com/#/location/49?locale=es-colD´Atlas. (2018). Medellín Met. Obtenido de http://datlascolombia.bancoldex.com/#/location/34Dai,M., Tang,D., Giret,A., Salido.M. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer Integrated Manufacturing, 59, 143–157. Recuperado de https://doi.org/10.1016/j.rcim.2019.04.006.DANE. (2020). Departamento Administrativo Nacional de Estadística. Encuesta mensual manufacturera con enfoque territorial (EMMET) Históricos. Obtenido de https://www.dane.gov.co/index.php/estadisticas-por-tema/industria/encuesta-mensual-manufacturera-con-enfoque-territorial-emmet/emmet-historicosDANE. (2020). Departamento Administrativo Nacional de Estadística. Producto Interno Bruto (PIB) Base 2015. Obtenido de https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-trimestralesDarmawan,M., Widhiarti,R., & Teniwut, Y. (2018). Green productivity improvement and sustainability assessment of the motorcycle tire production process: A case study. Journal of Cleaner Production , 191, 273- 282. Recuperado de https://doi.org/10.1016/j.jclepro.2018.04.228.Deb, K. (2001). Multi-objective optimization using evolutionary algorithms . London: Wiley & Sons.Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2), 182-197.Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601. Recuperado de http://doi.org/10.1109/TEVC.2013.2281535.Deliktas, D., Torkul,O., & Ustun,O. (2019). A flexible job shop cell scheduling with sequence-dependent family setup times and intercellular transportation times using conic scalarization method. International Transactions in Operational Research, 26, 2410–2431.Demir, Y., & İşleyen, S. (2014). An effective genetic algorithm for flexible job-shop scheduling with overlapping in operations. International Journal of Production Research, 52(13), 3905-3921.Dendena, B., & Corsi, S. (2015). The Environmental and Social Impact Assessment (ESIA): a further step towards an integrated assessment process. Journal of Cleaner Production, 108, 965–977. Recuperado de http://doi.org/10.1016/j.jclepro.2015.07.110.Deng,Q., Gong,G., Gong,X., Zhang,L.,Liu,X., & Ren,Q. (2017). A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling. Computational Intelligence and Neuroscience, 1-21. Recuperado de https://doi.org/10.1155/2017/5232518.DEusanio,M ., Zamagni,A., & Petti,L. (2019). Social sustainability and supply chain management: Methods and. Journal of Cleaner Production , 235, 178- 189. Recuperado de https://doi.org/10.1016/j.jclepro.2019.06.323.Dietz,J., & Mulder, H. (2020). A History of Organisation and ICT. En Enterprise Ontology. The Enterprise Engineering Series. Springer, Cham.Digalwar,A., Dambhare,S.,& Saraswat,S. (2020). Social sustainability assessment framework for indian manufacturing industry. Materials Today: Proceedings, 8(11), 1-8- Recuperado de https://doi.org/10.1016/j.matpr.2019.12.226.DNP. (2009). Departamento Nacional de Planeación. Metalmecánica. Obtenido de https://colaboracion.dnp.gov.co/CDT/Desarrollo%20Empresarial/metalmecanica.pdDNP. (2018). Departamento Nacional de Planeación. Política para el mejoramiento de la calidad del aire. Bogotá.DNP. (2017). Departamento Nacional de Planeación. Diagnóstico de Crecimiento Verde. Análisis macroeconómico y evaluación del potencial de crecimiento verde. Bogotá. Domínguez Machuca, J. A., Álvarez Gil, M. J., Domínguez Machuca, M. A., González, S. G., & Ruiz Jiménez, A. (1995). Dirección de operaciones-Aspectos estratégicos en la producción y los servicios. McGraw Hill, España.Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26(1), 29-41.Dóro, L., Pandolfo.,A., & Panosso,R . (2020). Developing a specific structured procedure to assess sustainability performance in manufacturing processes. Journal of Cleaner Production, 7(11), 1- 40. Recuperado de https://doi.org/10.1016/j.jclepro.2020.122404.Drechsel,D., Barlow,C., Bare,J., Jacobs,N.,& Henshaw,J. (2018). Historical evolution of regulatory standards for occupational and consumer exposures to industrial talc. Regulatory Toxicology and Pharmacology, 92, 251–267. Recuperado de https://doi.org/10.1016/j.yrtph.2017.12.005.Earnhart, D., & Leonard, J. (2016). Environmental audits and signaling: The role of firm organizational structure. Resource and Energy Economics, 44(16), 1-22.Ebrahimi,A., Jeon,H., & Leeb, C. (2020). Minimizing total energy cost and tardiness penalty for a scheduling-layout problem in a flexible job shop system: A comparison of four metaheuristic algorithms. Computers & Industrial Engineering , 141, 1-21. Recuperado https://doi.org/10.1016/j.cie.2020.106295.Elkington, J. (1994). Towards the sustainable corporation: win-win-win business strategies for sustainable development. Calif. Manag. Rev, 36, 90 - 100.Elkington, J. (1998). Partnerships from Cannibals with Forks: the triple bottom line of 21st-century business. Environ. Qual. Manag, 8(11), 37- 51.EPA. (2017). Agencia de Proteccion Ambiental de los Estados Unidos. Descripción general de los gases de efecto invernadero. Obtenido de https://www.epa.gov/ghgemissions/overview-greenhouse-gasesErvurala, B., Zaima,S., Demireld, O., Aydinb,Z., & Dursun,D. (2018). An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey’s energy planning. Renewable and Sustainable Energy Reviews, 82, 1538–1550.FACTS. (2015). Agencia Europea para la Seguridad y la Salud en el Trabajo. Los efectos del ruido en el trabajo. España.Fazel Zarandi, M.H., Sadat Asl, A.A., Sotudian, S., Castillo, O., 2020. A state of the art review of intelligent scheduling, Artificial Intelligence Review. Springer Netherlands, 53, 501-593. https://doi.org/10.1007/s10462-018-9667-6Fadil,A., Chetouane,F & Binder,Z. (1997). Control of flexible job-shop: disturbances and robustness. IFAC Management and Control of Production and Logistics, 13-18.Famiyeh, S., Kwarteng, A., Asante-Darko, D., y Dadzie, S. A. (2018). Green supply chain management initiatives and operational competitive performance. Benchmarking, 25(2), 607–631. https://doi.org/10.1108/BIJ-10-2016-0165Fasecolda. (2020). Reporte por clase de riesgo y actividad económica - Consolidado. Obtenido de https://sistemas.fasecolda.com/rldatos/Reportes/xClaseGrupoActividad.aspxFerreira,V., Pinto,O., Arroyo,R., & Castro,E. (2018). Berry color variation in grapevine as a source of diversity. Plant Physiology and Biochemistry, 132, 696–707. Recuperado de https://doi.org/10.1016/j.plaphy.2018.08.021.Fitzpatrick, J. (2016). Environmental sustainability assessment of using forest wood for heat energy in Ireland. Renewable and Sustainable Energy Reviews, 57, 1287–1295. Recuperado de http://doi.org/10.1016/j.rser.2015.12.197.Fleming,J. (1985). Computer aided design of regulators using multiobjective optimization. Annual Review in Automatic Programming, 13(2), 47-52. Recuperado de https://doi.org/10.1016/0066-4138(85)90462-8.Gao, J., Gen, M., & Sun, L. (2007). Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm. Journal of Intelligent Manufacturing, 17(4), 493–507. Recuperado de http://doi.org/10.1007/s10845-005-0021-x.Gao, K., Suganthan, P., Pan, Q., Chua, T., Cai, T., & Chong, C. (2014). Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling. Information Sciences, 289(67), 76–90. Recuperado de http://doi.org/10.1016/j.ins.2014.07.039.Gao, K., Suganthan, P., Pan, Q., Chua, T., Chong, C., & Cai, T. (2016a). An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Systems with Applications, 65, 52-67. doi: 10.1016/j.eswa.2016.07.046.Gao, L., Zhou, B., Yang, L., & Wang, J. (2015). A multi-objective integrated optimization method for fjsp based on multi-rule resource allocation. Journal of Shanghai jiaotong University, 49(08), 1191–1198. Recuperado de http://xuebao.sjtu.edu.cn/CN/abstract/abstract10626.shtml.Gao,J., Gen,M., Sun,L., & Zao,X. (2007a). A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computers & Industrial Engineering, 53, 149–162.Gao,K., Suganthan,P., Chua,T., Chong,C., Cai,T., & Pan, Q. (2015a). A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert Systems with Applications , 42, 7652–7663.Gao,K., Suganthan,P., Pan,Q., Tasgetiren,M., & Sadollah,A. (2016b). Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion. Knowle dge-Base d Systems, 109, 1–16.Gao,K., Suganthan,P., Tasgetiren,M., Pan,Q., & Sun,Q. (2015b). Effective ensembles of heuristics for scheduling flexible job shop problem with new job insertion. Computers & Industrial Engineering , 90, 107–117.Gao,Z., Sun,D., Zhao, R., & Dong, Y. (2021). Ship-unloading scheduling optimization for a steel plant. Information Sciences, 544, 214–226.Gao,L.,& Pan,Q. (2016). A shuffled multi-swarm micro-migrating birds optimizer for a multi-resource-constrained flexible job shop scheduling problem. Information Sciences , 372, 655–676.Garcia,S., Cintra,Y., Torres,C ., Guasti,F. (2016). Corporate sustainability management: a proposed multi-criteria model to support balanced decision-making. Journal of Cleaner Production, 136 (2016) 181-196. Recuperado de http://dx.doi.org/10.1016/j.cor.2016.03.009.Geoffrion, A., Dyer, J., & Feinberg, A. (1972). An interactive approach for multi-criterion optimization, with an application to the operation of an academic department. Management Science, 19(4), 357-368. Recuperado de https://doi.org/10.1287/mnsc.19.4.357.Geyik,F., & Dosdog˘ru,T. (2013). Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach. Neural Comput & Applic, 23, 1631–1641.doi: 10.1007/s00521-012-1119-7.Ginting,R., Wanli., & Malik,A. (2020). Crude Palm Oil Product Quality Control Using Seven Tools (case study:XYZ Company). IOP Conf. Series: Materials Science and Engineering, 1-9, doi:10.1088/1757-899X/851/1/012046.Giraldo, J.A., Castrillón, O.D., & Ruiz, S. (2019). Discrete Simulation and Agents of a Simple Chain Supply including a Geographic Information System (GIS). . Revista Información Tecnológica, 30(6), 123-136. Recuperado de http://dx.doi.org/10.4067/S0718-07642019000600123.Giret, A., Trentesaux, D., & Prabhuc, V., (2015). Sustainability in manufacturing operations scheduling: a state of the art review. J. Manuf. Syst. 37, 126e140.Glover,F & Kochenberger,G. (2010). Handbook of Metaheuristics. Boston: Springer. Recuperado de http://doi.org/10.1007/978-1- 4419-1665-5.Goldberg,D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Boston: Addison-Wesley Longman Publishing.Gómez, P. (2010). Programación de la producción en un taller de flujo híbrido sujeto a incertidumbre: arquitectura y algoritmos. España: Universidad Politécnica de Valencia.Gong,G., Deng,Q., Gong,X., Liu, W., & Ren,Q. (2018). A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators. Journal of Cleaner Production, 174, 560- 576. Recuperado de https://doi.org/10.1016/j.jclepro.2017.10.188.Gong,X., Deng,Q., Gong,G., Liu,W., & Ren,Q. (2017). A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility. International Journal of Production Research, 56 (7)., 2506–2522, Recuperhttps://doi.org/10.1080/00207543.2017.1388933.Gong,V., Van der Wee,M., De Pessemier,T., Verbrugge,S., Colle,D., & Martens,L. (2017a). Energy- and labor-aware production scheduling for sustainable manufacturing: A case study on plastic bottle manufacturing. 24th CIRP Conference on Life Cycle Engineering, 2212-8271, doi: 10.1016/j.procir.2016.11.136.Gracia,J.,Lara,L., Quintero,D., & Santis,A. (2018). Formulation of Strategies for the Implementation of Integral 14001:2015 in the Company Surtiapliques (Bogotá-Colombia). Chemical Engineering Transactions, 12, 559-564. doi: 10.3303/CET1867094.Grossmann, E., Halemane, K., & Swaney, R. (1983). Optimization strategies for flexible chemical processes. Computers and Chemical Engineering, 7(4), 439-462. Recuperado de https://doi.org/10.1016/0098-1354(83)80022-2.Gu,X., Huang,M., & Liang, X. (2020). A Discrete Particle Swarm Optimization Algorithm With Adaptive Inertia Weight for Solving Multiobjective Flexible Job-shop Scheduling Problem. IEEE Access, 8, 33125- 33136.Guía País - Kenia. (2012). España: Ministerio de Asuntos Exteriores y Cooperación .Gupta, S.M., 2016. Lean manufacturing, green manufacturing and sustainability. J. Japan Ind. Manag. Assoc. 67, 102–105. https://doi.org/10.11221/jima.67.102Gutiérrez,C & García,I. (2011). Modular design of a hybrid genetic algorithm for a flexible job–shop scheduling problem. Knowledge-Based Systems, 24, 102–112. doi:10.1016/j.knosys.2010.07.010.Hadka,D., & Reed,P. (2012). Diagnostic Assessment of Search Controls and Failure Modes in Many-Objective Evolutionary Optimization. Evolutionary Computation, 20(3), 423–452.Haitham,S.,& and Deb,H. (2014). U-NSGA-III: A Unified Evolutionary Algorithm for Single,Multiple, and Many-Objective Optimization. Computational Optimization and Innovation Laboratory, 5(12), 1-30.Hale,J., Legun,J., Campbell,H.,& Carolan,M. (2019). Social sustainability indicators as performance. Geoforum , 103, 47–55. Recuperado de https://doi.org/10.1016/j.geoforum.2019.03.008.Haugeland, J. (1985). Artificial Intelligence: The Very Idea. Cambridge: MIT Press.He, W., & Sun, D. (2012). Scheduling flexible job shop problem subject to machine breakdown with route changing and right-shift strategies. . The International Journal of Advanced Manufacturing Technology, 66(1-4), 501–514. http://doi.org/10.1007/s00170-012-4344-4.He,Y., Li,Y., Wu,T., Sutherland,J. (2015). An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. Journal of Cleaner Production, 87, 245-254. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2014.10.006.Herrero,M.,& Stuckey,D. (2015). Bioaugmentation and its application in wastewater treatment: A review. Chemosphere, 140 , 119–128. Recuperado de http://dx.doi.org/10.1016/j.chemosphere.2014.10.033.Holcombe, H. (2018). Queer After All: Middlesex, the Ford Factory, and the Genetic History of Incest. Arizona Quarterly. Journal of American Literature, Culture, and Theory , 74(2), 1-37. doi:10.1353/arq.2018.0007.Holland, J. H. (1975). Adaptation in natural and artificial systems.The University of Michigan Press, Englewood Cliffs, 66–72.Hongbo,L.,, Ajith,A., Zuwen,W. (2009). A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems. Fundamenta Informaticae , 95 (4), 465-489.Hongfeng,W., Yaping,F., Min,H., George,Q ., Huang,B ., Junwei,W. (2017). A NSGA-II based memetic algorithm for multiobjective parallel flowshop scheduling problem. Computers & Industrial Engineering, 113, 185–194. Recuperado de http://dx.doi.org/10.1016/j.cie.2017.09.009.Hongtao,T., Rong,C,, Yibing,L., Zhao,P-. Shunsheng,G.,&Yuzhu,D. (2019). Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop. Applied Soft Computing Journal, 78, 176–194. Recuperado de https://doi.org/10.1016/j.asoc.2019.02.011.Hormozi,H., Zhang, Z., Zarei,O., & Ramezanian, R. (2018). Trade-off between the costs and the fairness for a collaborative production planning problem in make-to-order manufacturing. Computers & Industrial Engineering, 126, 421-434. recuperado de https://doi.org/10.1016/j.cie.2018.09.044.Horn, J., Nafpliotis, N., & Goldberg, D. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, Orlando, FL, 82-87. doi: 10.1109/ICEC.1994.350037.Hossain,S., Galbreath.J., Monzur,M., & Randøy,T. (2020). Does competition enhance the double-bottom-line performance of microfinance institutions? Journal of Banking and Finance 113 (2020) 105765, 113, 1-17. Recuperdo de https://doi.org/10.1016/j.jbankfin.2020.105765.Hosseinabadi, R., Siar, H., Shamshirband, S., Shojafar, M., & Nasir, M. (2014). Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterpris. Annals of Operations Research, 229(1), 451-474. doi: 10.1007/s10479-014-1770-8.Hu,C., Dai, L., Yan,X., Gong,W., Liu,X., & Wang,L. (2020). Modified NSGA-III for sensor placement in water distribution system. Information Sciences, 509, 488–500. Recuperado de https://doi.org/10.1016/j.ins.2018.06.055.Huang, J., & Süer, G. (2015). A dispatching rule-based genetic algorithm for multi-objective job shop scheduling using fuzzy satisfaction levels. Computers & Industrial Engineering, 86, 29–42. Recuperado de http://doi.org/10.1016/j.cie.2014.12.001.Huang, R., Yang, C., & Cheng, W. (2013). Flexible job shop scheduling with due window-a two-pheromone ant colony approach. International Journal of Production Economics, 141(2), 685–697. Recuperado de http://doi.org/10.1016/j.ijpe.2012.10.011.Huang, S., Tian, N., Wang, Y., & Ji, Z. (2016a). Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization. Springer Plus, 5 (1). doi: 10.1186/s40064-016-3054-z.Huang, W., Zhao. Y. & Ma, X. (2014). An improved genetic algorithm for job-shop scheduling problem with process sequence flexibility. International . Journal of Simulation Modeling, 13(4), 510-522. Recuperado de http://m.ijsimm.com/Full_Papers/Fulltext2014/text.Huang, X., & Yang, L. (2019). A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time. International Journal of Intelligent Computing and Cybernetics, 12 (2), 154-174.Huang,D.,Tian,N.,& Ji, W. (2016b). Particle swarm optimization with variable neighborhood search for multiobjective flexible job shop scheduling problem. International Journal of Modeling, Simulation, and Scientific Computing, 7(3). Recuperado de https://doi.org/10.1142/S1793962316500240Hui,L., Rongrong,Z., , Shi,C., & Yuhui,S. (2019). Multi-center variable-scale search algorithm for combinatorial optimization problems with the multimodal property. Applied Soft Computing Journal, 84, 105-116. Recuperado de https://doi.org/10.1016/j.asoc.2019.105726.Huot, H., Séré, G., Charbonnier, P., Simonnot, M., & Morel, J. (2015). Lysimeter monitoring as assessment of the potential for revegetation to manage former iron industry settling ponds. The Science of the Total Environment, 526, 29–40. Recuperado de http://doi.org/10.1016/j.scitotenv.2015.04.025.Hutchins,M., & Sutherland,J. (2008). An exploration of measures of social sustainability and their application to supply chain decisions. Journal of Cleaner Production, 16 (15), 1688-1698. Recuperado de https://doi.org/10.1016/j.jclepro.2008.06.001.Hutchins,M., Richter,J., Henry,M., & Sutherland,J. (2019). Development of indicators for the social dimension of sustainability in a U.S. business context. Journal of Cleaner Production , 212, 687- 697. Recuperado de https://doi.org/10.1016/j.jclepro.2018.11.199.Hutomo, M; Haizam., & Sinaga. (2018). The Mediating Role of Organizational Learning Capability On Green Distribution and Green Packaging TowardsSustainability Performance as A Function Environmental. Dynamism: Indonesia and Malaysia Fishery Industries. Conf. Series: Earth and Environmental Science , 164,1-12.Ibrahim,A., Vargas,M., Rahnamayan,S., & Deb,K. (2016). EliteNSGA-III: An Improved Evolutionary Many-Objective Optimization Algorithm. WCCI, 11(7),1-10.IEA. (2019). Emisiones globales de CO2 en 2019. Obtenido de https://www.iea.org/articles/global-co2-emissions-in-2019Ishibuchi,H., & Murata. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 392-403. doi: doi: 10.1109/5326.704576.Jain,V., Singhal,A., Sachdeva,G., & Kachhwaha. (224 (2020) 113348). Advanced exergy analysis and risk estimation of novel NH3-H2O and H2O-LiBr integrated vapor absorption refrigeration system. Energy Conversion and Management, 224, 2-16.Jalali, T., Jafari,M.,& Mohammadi,A. (2018). Genetic algorithm optimization of antireflection coating consisting of nanostructured thin films to enhance silicon solar cell efficacy. Materials Science & Engineering B , 247, 1-8. Recuperado de https://doi.org/10.1016/j.mseb.2019.05.016.Jarke,M., & Radermacher,F. (1988). The AI potential of model management and its central role in decision support. Decision Support Systems, 387-404, Recuperado de https://doi.org/10.1016/0167-9236(88)90002-4.Jeyadebi,S., Baskar,S., Babula,S., & Iruthayarajan,W. (2011). Solving multiobjective optimal reactive power dispatch using modified NSGA-II. International Journal of Electrical Power & Energy Systems, 33(2), 219-228, Recuperado de https://doi.org/10.1016/j.ijepes.2010.08.017.Jia, S., & Hu, Z. (2014). Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem. . Computers & Operations Research, 47, 11–26. Recuperado de http://doi.org/10.1016/j.cor.2014.01.010.Jia, Y., & Liu, R. (2012). Study of the energy and environmental efficiency of the Chinese economy based on a DEA model. Procedia Environmental Sciences, 13, 2256–2263. Recuperado de http://doi.org/10.1016/j.proenv.2012.01.214.Jia, Z., Zhu, J., & Chen, H. (2012). Dynamic tabu particle swarm optimization algorithm for flexible job-shop scheduling. Journal of South China University of Technology, 40(1). Recuperado de https://scholar.google.es/scholar?q=Dynamic+tabu+particle+swarm+optimization+algorithm+for+flexible+job-shop+scheduling++&btnG=&hl=en&as_sdt=0%2C5#0.Jia,F., Yin,S., Chen,L.,& Chen,X. (2020). The circular economy in the textile and apparel industry: A systematic literature review. Contents lists available at Science Direct, 12(21), 1-20. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120728.Jia,Z., Pei,M & Leung,J. (2017). Multi-objective ACO algorithms to minimise the makespan and the total rejection cost on BPMs with arbitrary job weights. International Journal of Systems Science, 48(16), 3542-3557. Recuperado de https://doi.org/10.1080/00207721.2017.1387314.Jiang, Z., Zuo, L., & E, M. . (2014). Study on multi-objective flexible job-shop scheduling problem considering energy consumption. Journal of Industrial Engineering and Management, 7(3), 589–604. Recuperado de http://doi.org/10.3926/jiem.1075.Jiang,E., & Wang,L. (2020). Multi-objective optimization based on decomposition for flexible job shop scheduling under time-of-use electricity prices. Knowledge-Based Systems, 204, 106- 177.Jiang,M., Zeng,M., Cao, Z.,Bi,Y., Wang, L., Wang,Y. (2019). The history, logic and trends of the discipline of safety science in China. Safety Science , 116, 37–148. Recuperado https://doi.org/10.1016/j.ssci.2019.03.005.Jiang,T & Deng,G. (2018). Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem Considering Energy Consumption. IEEE Access, 6, 46346- 46354. doi:0.1109/ACCESS.2018.2866133.Jiao,Z., Yuan, S., Ji,C., Mannan,S., & Wang,Q. (2019). Optimization of dilution ventilation layout design in confined environments. Journal of Loss Prevention in the Process Industries, 60, 195–202, Recuperado de https://doi.org/10.1016/j.jlp.2019.05.002.Joc, T., & Nhu, H. (2008). Evolving dispatching rules using genetic programming for solving multiobjective flexible job shop problems. Computers & Industrial Engineering , 54 (22), 453–473.Jones,J., Bleasdale,S., Maita,D., & Brosseau,L. (2020). A systematic risk-based strategy to select personal protective equipment for infectious diseases. American Journal of Infection Control, 48, 46−51. Recuperado de https://doi.org/10.1016/j.ajic.2019.06.023.Kacem, I., Hammadi, S., & Borne, P. (2002). Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation, 60(3-5), 245–276. recuperado de http://doi.org/10.1016/S0378-4754(02)00019-8.Kaplanoğlu, V. (2016). An object-oriented approach for multi-objective flexible job-shop scheduling problem. Expert Systems with Applications, 45, 71–84. http://doi.org/10.1016/j.eswa.2015.09.050, 45, 71–84.Karimi-Nasab, M., Modarres, M., & Seyedhoseini, S. (2015). A self-adaptive PSO for joint lot sizing and job shop scheduling with compressible process times. Applied Soft Computing, 27, 137–147. http://doi.org/10.1016/j.asoc.2014.10.015, 27, 137–147. Recuperado de http://doi.org/10.1016/j.asoc.2014.10.015.Karthikeyan, S., Asokan, P., & Chandrasekaran, M. (2014a). A Hybrid Discrete Firefly Algorithm for Multi-Objective Flexible Job Shop Scheduling Problems with Maintenance Activity. Applied Mechanics and Materials, 575, 922–925. http://doi.org/10.4028/www.scientific.net/AMM.575.922.Karthikeyan, S., Asokan, P., & Nickolas, S. (2014b). A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints. The International Journal of Advanced Manufacturing Technology, 72 (9), 9-12. Obtenido de http://doi.org/10.1007/s00170-014-5753-3.Kay Camarillo, M., Stringfellow, W. T., Jue, M. B., & Hanlon, J. . (2012). Economic sustainability of a biomass energy project located at a dairy in California, USA. . Energy Policy, 48, 790–798. Recuperado de http://doi.org/10.1016/j.enpol.2012.06.020.Kaya, S. & Figlali, N. (2013). Multi Objective Flexible Job Shop Scheduling Problems. Journal of Engineering and Natural Sciences, 31, 605-623. Recuperado de http://eds.yildiz.edu.tr/ArticleContent/Journal/sigma/Volumes/2013/Issues/Regular-4/YTUJENS-20134/YTUJENS-2013-31-4.485.pdf .Kempen,E., Casas,M., Pershagen,G., & Foraster,M. (2018). WHO Environmental Noise Guidelines for the European Region: A Systematic Review on Environmental Noise and Cardiovascular and Metabolic Effects: A Summary. International Journal of Environmental Research and Public Health, 15 (379), 1.59. doi:10.3390/ijerph15020379.Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks, 4, 1942–1948.Khan,I. (2020). Critiquing social impact assessments: Ornamentation or reality in the Bangladeshi electricity infrastructure sector? Energy Research & Social Science , 60 (10), 1-15. Recuperado de https://doi.org/10.1016/j.erss.2019.101339.Khosravi,F.,& Izbirak,G. (2019). A stakeholder perspective of social sustainability measurement in healthcare. Sustainable Cities and Society , 50, 1-12. Recuperado de https://doi.org/10.1016/j.scs.2019.101681.Kiourtisa,A.,Nifakosb,S., Mavrogiorgoua,A., & Kyriazisa,D. (2019). Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching. International Journal of Medical Informatics, 132, 1-16. Recuperado de https://doi.org/10.1016/j.ijmedinf.2019.104002.Korhonen, P.., & Laakso, J. (1986). A visual interactive method for solving the multiple criteria problem (VIA). European Journal of Operational Research, 24(2), 277-287. Recuperado de https://doi.org/10.1016/0377-2217(86)90050-0.Kroppa,I., Nejadhashemia,A.,Deb,K., Aboualia,M., Royc,P., Adhikaria, U., & Hoogenboomd,G. (2019). A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification. Agricultural Systems, 173, 289–302. Recuperado de https://doi.org/10.1016/j.agsy.2019.03.014.Kudryavtsev,S., Yemelin,P., & Yemelina,N. (2018). The Development of a Risk Management System in the Field of Industrial Safety in the Republic of Kazakhstan. Safety and Health at Work, 9, 30-41. Recuperado de http://dx.doi.org/10.1016/j.shaw.2017.06.003.Kumar,A., & Anbanandam,R. (2019). Development of social sustainability index for freight transportation system. Journal of Cleaner Production, 210, 77- 92. Recuperado de https://doi.org/10.1016/j.jclepro.2018.10.353.Kvam,P., & Hintze,A. (2018). Rewards, risks, and reaching the right strategy: Evolutionary paths from heuristics to optimal decisions. Research Gate, 1-13. Recuperado de https://www.researchgate.net/publication/321106854.Kyoung,M., Jin,Y. (2017). Exposure to environmental noise and risk for male infertility: A population-based cohort study. Environmental Pollution, 226: 118. doi 10.1016/j.envpol.2017.03.069. Lacomme, P., Larabi, M., & Tchernev, N. (2013). Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Economics, 143(1), 24–34. recuperado de http://doi.org/10.1016/j.ijpe.2010.07.012.Laosiritaworn,W., Pinchai, N., & Parupuncharadsri,N. (2020). Job Shop Layout Improvement with Simulation Technique: A Case of. IOP Conference Series: Materials Science and Engineering, 1-6. Laukkanen,M. (2019). Sustainable business models for advancing system-level sustainability. London: Acta Universitatis.Lavicoli,A., Gambelunghe , A., Magrini, G., Mosconi d, L., Soleo, L., Vigna, A., Trevisan, A., Bruno, M., Chiambretti,M., Scarpitta, L., Sciacca, U & Valentini. (2019). Diabetes and work: The need of a close collaboration between diabetologist and occupational physician. Nutrition, Metabolism & Cardiovascular Diseases, 29, 220-227. Recuperado de https://doi.org/10.1016/j.numecd.2018.10.012.Lei, D., & Guo, X. (2015). An effective neighborhood search for scheduling in dual-resource constrained interval job shop with environmental objective. International Journal of Production Economics, 159, 296–303. Recuperado de http://doi.org/10.1016/j.ijpe.2014.07.026.Lei,D., Li,M., & Wang,L. (2019). A Two-Phase Meta-Heuristic for Multiobjective Flexible Job Shop Scheduling Problem With Total Energy Consumption Threshold. IEEE Transactions on Cybernetics , 49(3), 1097-1109,doi: 10.1109 / TCYB.2018.2796119.Leyland, G. (2002). Multi-objective optimisation applied to Industrial Energy Problems. Auckland - Nueva Zelanda: Universidad de Auckland.Li, C., Cui, H., & Wang, G. (2013a). The Optimization of Flexible Job-Shop Scheduling Problem Based on NSGA-II. Advanced Materials Research, 651(3), 684-687. Recuperado de http://www.scientific.net/AMR.651.684.Li, J., & Pan, Q. (2013). Chemical-reaction optimization for solving fuzzy job-shop scheduling problem with flexible maintenance activities. International Journal of Production Economics, 145(1), 4–17. Recuperado de http://doi.org/10.1016/j.ijpe.2012.11.005.Li, J., Duan, P., Cao, J., X., Lin,X.,& Han,Y. (2018a). A Hybrid Pareto-Based Tabu Search for the Distributed Flexible Job Shop Scheduling Problem With E/T Criteria. IEEE Access, 58883-58897, doi: 10.1109/ACCESS.2018.2873401.Li, J., Pan, Q., & Liang, Y. (2010a). An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 59(4), 647–662. Recuperado de http://doi.org/10.1016/j.cie.2010.07.014.Li, J., Pan, Q., & Xie, S. (2012a). An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Applied Mathematics and Computation, 218(18), 9353–9371. Recuperado de http://doi.org/10.1016/j.amc.2012.03.018.Li, J.-Q., Pan, Q.-K., & Chen, J. (2012b). A hybrid Pareto-based local search algorithm for multi-objective flexible job shop scheduling problems. International Journal of Production Research, 50(4), 1063–1078. Recuperado de http://doi.org/10.1080/00207543.2011.555427.Li, J.-Q., Pan, Q.-K., & Tasgetiren, M. (2014). A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Applied Mathematical Modelling, 38(3), 1111–1132. Recuperado de http://doi.org/10.101.Li, L., & Huo, J. (2009). Multi-Objective Flexible Job-Shop Scheduling Problem in Steel Tubes Production. Systems Engineering - Theory & Practice, 29(8), 117–126. Recuperado de http://doi.org/10.1016/S1874-8651(10)60063-4.Li,B., Guo, C.,& Ning, T. (2018b). An improved bacterial foraging optimization for multi-objective flexible job-shop scheduling problem(Article). Journal Europeen des Systemes Automatises, 51(8),323-332.Li,H., Zhu,H., & Jiang,T. (2020a). Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem. Algorithms , 23(12), 13-44. recuperado de doi:10.3390/a13020044.Li,J., Deng,J., Li,C., Han,Y., Tian,J., Zhang,B.,& Wang,C. (2020b). An improved Jaya algorithm for solving the flexible job shop scheduling problem with transportation and setup times. Knowledge-Based Systems, 200.1-12. Recuperado de https://doi.org/10.1016/j.knosys.2020.106032.Li, J., & Pan, Q. (2012). Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity. Applied Soft Computing, 12(9), 2896–2912. Recuperado de http://doi.org/10.1016/j.asoc.2012.04.012.Li, J., Pan,Q., & Gao,K. (2011a). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Technol, 4(5), :1159–1169.Li, J., Paq,Q., Xie,S., Wang,S. (2011b). A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems. Int. J. of Computers, Communications & Control, 6(2), 286-296.Li, J., Pan,Q., Wang,F. (2014a). A hybrid variable neighborhood search for solving the hybrid flowshop scheduling problem. Applied Soft Computing , 24(12), 63–77.Li,U.,Pan,Q., Wang, Y. (2010b). Hybrid Pareto-based tabu search algorithm for solving the multi-objective flexible Job Shop scheduling problem. Computer Integrated Manufacturing Systems, 16(7), 1419-1426.Li,X., Peng,Z., Dub, B., Guo,J., Xu,W., Zhuang, K . (2016). Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems. Computers & Industrial Engineering, 113 (2017) 10–26. Recuperdo de http://dx.doi.org/10.1016/j.cie.2017.09.005.Li,X.,& Gao,L. (2016). An effective hybrid genetic algorithm and tabu searchfor flexiblejob shop schedulingproblem. Int. J.Production Economics, 174, 93–110. Recuperado http://dx.doi.org/10.1016/j.ijpe.2016.01.016.Li,Y., He,Y., Wang,Y.,Tao,F., & Sutherland,J. (2020c). An optimization method for energy-conscious production in flexible machining job shops with dynamic job arrivals and machine breakdowns. Journal of Cleaner Production , 254, 1- 14. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120009.Li,Z., Qian,B., Hu,R., Chang,L.,& Yang,J. (2019). An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups. Knowledge-Based Systems, 173, 83–112.Lin,J., Pan,Q., & Liang,Y. (2010). An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 59, 647–662.Liu, A., Ya, Yang., Xing, Q., & Lu, H. (2011a). Dynamic scheduling on multi-objective flexible Job Shop. Computer Integrated Manufacturing Systems, 17(12), 2629-2637.Liu, A., Ya, Yang., Xing, Q., Lu, H., & Zhang, Y. (2011b). Multi-population genetic algorithm in multi-objective fuzzy and flexible Job Shop scheduling. Computer Integrated Manufacturing Systems,17(9), 1954-1961.Liu, B., Fan, Y., & Liu, Y. (2015a). A fast estimation of distribution algorithm for dynamic fuzzy flexible job-shop scheduling problem. Computers & Industrial Engineering, 87, 193–201. Recuperado de http://doi.org/10.1016/j.cie.2015.04.029 .Liu, Q., Tian,Y., Wang, C., Chekem, F., & Sutherland, J. (2018). Flexible job-shop scheduling for reduced manufacturing carbon footprint. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 140(6), 21-39.Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2015b). Reducing environmental impact of production during a Rolling Blackout policy – A multi-objective schedule optimisation approach. Journal of Cleaner Production, 102, 418–427. Recuperado de http://doi.org/10.1016/j.jclepro.2015.04.038.Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2016). A multi-objective genetical objective genetic algorithm for optimisation of energy consumption and shop floor production performance. Int. J. Production Economics, 179(2016)259–272. Recuperado de http://dx.doi.org/10.1016/j.ijpe.2016.06.019.Liu,D., Huang,Q., Yang,Y., Liu, D.,& Wei,X. (2020). Bi-objective algorithm based on NSGA-II framework to optimize reservoirs operation. Journal of Hydrology, 585, 1-12. Recuperado de https://doi.org/10.1016/j.jhydrol.2020.124830.Liu,H., Chena,C., Lv,X., Wuc,X ., & Liud,M. (2019). Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Conversion and Management, 195 , 328–345. Recuperado de https://doi.org/10.1016/j.enconman.2019.05.020.Liu,N., Zhang,Y., & Feng,W. (2019b). Improving Energy Efficiency in Discrete Parts Manufacturing System Using an Ultra Flexible Job Shop Scheduling Algorithm. International Journal of Precision Engineering and Manufacturing-Green Technology, 6:349–365. recuperado de https://doi.org/10.1007/s40684-019-00055-y.Liu,Q., Zhan,M .,Chekem,F.,Shao,X .,Ying,B., & Sutherland,J. (2017). A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint. Journal of Cleaner Production, 168, 668-678. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2017.09.037.Liu,T., Chen,Y.,& Chou,H. (2014). Developing a Multiobjective Optimization Scheduling System for a Screw Manufacturer: A Refined Genetic Algorithm Approach. IEEE Aceess, 2(11),356- 364.Liu,Y.,Dong,H., Lohse,N., & Petrovic,S. (2016). A multi-objective genetic algorithm fo roptimisation of energy consumption and shop floor production performance. Int. J.ProductionEconomics, 179(, 259–272. Obtenido de http://dx.doi.org/10.1016/j.ijpe.2016.06.019.Liu, Z., Guo,S., & Wang,L. (2019a). Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. Journal of Cleaner Production, 211, 765- 786. Recuperado de https://doi.org/10.1016/j.jclepro.2018.11.231.Liu, Z., Yan,J., Cheng,Q., Yang,C., & Sun,S. (2020). The mixed production mode considering continuous and intermittent processing for an energy-efficient hybrid flow shop scheduling. Journal of Cleaner Production , 246, 1-17. Recuperado de https://doi.org/10.1016/j.jclepro.2019.119071.Liu, Q., Panb,Q., Gaoa,L., & Lia,X. (2019). Multi-Objective Flexible Job Shop Scheduling Problem Considering Machine Switching Off-On Operation. Procedia Manufacturing, 39, 1167–1176.López, M., Díaz Flores.,& Winkler,R. (2018). Increase of peroxidase activity in tropical maize after recurrent selection to storage pest resistance. Journal of Stored Products Research, 75, 47- 55. Recuperado de https://doi.org/10.1016/j.jspr.2017.11.007.Lozano, S., & Calzada, L. (2019). Efficiency ranking using dominance network and multiobjective optimization indexes. Expert Systems with Applications, 126, 83-91. Recuperado de https://doi.org/10.1016/j.eswa.2019.02.016.Lu, C., Li, X., Gao, L., Liao, W., & Yi, J. (2017). An effective multi-objective discrete virus optimization algorithm for flexible job-shop scheduling problem with controllable processing times. Computers & Industrial Engineering , 104, 156–174. Recuperado de http://dx.doi.org/10.1016/j.cie.2016.12.020.Lu, K., Ting, L., Keming, W., Hanbing, Z., Makoto, T., & Bin, Y. (2015). An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem. Algorithms. Algorithms, 8(1), 19-3. http://www.mdpi.com/1999-4893/8/1/19/htm.Lu, Y., & Zhang, X. (2015). Corporate sustainability for architecture engineering and construction (AEC) organizations: Framework, transition and implication strategies. Ecological Indicators, 61, 911–922. http://doi.org/10.1016/j.ecolind.2015.10.046.Luger, F & Stubblefield, W. (1993). Artificial Intelligence Structures and Strategies for Complex Problem Solving. Palo Alto: Benjamin Cummings.Luger, G. (2002). Artificial Intelligence: Structures and Strategies for Complex Problem Solving . London: Addison-Wesley Longman.Lui, Q., Li, X., Liu, H., & Guo, Z. (2020). Multi-objective metaheuristics for discrete optimization problems: A review of the state-of-the-art. . Applied Soft Computing Journal, , 93, 1-16. Recuperado de https://doi.org/10.1016/j.asoc.2020.106382.Luo,D., Chen,H.,Wu,S., Shi, Y. (2010). Hybrid ant colony multi-objective optimization for flexible job shop scheduling problems. Journal of Internet Technology, 11(3), 361-370.Luo,S., Zhang,L., & Fan,Y. (2019). Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. Journal of Cleaner Production 234 (2019), 1365-1384. Recuperado de https://doi.org/10.1016/j.jclepro.2019.06.151.Macias,S., & Sakao,T. (2020). Effective ecodesign implementation with the support of a lifecycle engineer. Journal of Cleaner Production, 279, 1-10.Magazzino, C., Mele, M., Schneider, N., & Sarkodie, S.A., 2020. Waste generation, Wealth and GHG emissions from the waste sector: Is Denmark on the path towards Circular Economy? Sci. Total Environ. 755, 142510. https://doi.org/10.1016/j.scitotenv.2020.142510Maes,P. (1993). Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back. London: Firet MIT Press edition .Mahmud,M.,Abidina,A., Mohamed,Z.,Rahman,M., & Lida,M. (2019). Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment. Computers and Electronics in Agriculture, 157, 488-499. recuperado de https://doi.org/10.1016/j.compag.2019.01.016.Mahzun, R., & Kalalo, F. (2019). The environmental aspect and impact assessment for heavy industries: Empirical study on steel fabrication and shipyard operations in batam Indonesia. Quality - Access to Success, 20 (172), 108-113.Maiera,H., Razavib,S., Kapeland,Z., Matotte,L., & Kasprzykf, B. (2019). Introductory overview: Optimization using evolutionary algorithms and other metaheuristics. Environmental Modelling and Software , 114 (, 195–213. Recuperado de https://doi.org/10.1016/j.envsoft.2018.11.018.Malek,J & Desai,T. (2020). A systematic literature review to map literature focus of sustainable manufacturing. Journal of Cleaner Production, 256,1-20. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120345.Mandal,P., Perumal,G., Arora,H., & Ghosh,S. (2020). Green manufacturing of nanostructured Al-Based sustainable selfcleaning metallic surfaces. Journal of Cleaner Production, 278, 1-10.Mani, V., Agrawal, R., & Sharma, V. (2015). Social sustainability practices in the supply chain of Indian manufacturing industries. International Journal of Automation and Logistics,, 1(3), 211 - 233.Mani,V., Chiappetta,C.,& Mani,J. (2020). Supply chain social sustainability in small and medium manufacturing enterprises and firms’ performance: Empirical evidence from an emerging Asian economy. Int. J. Production Economics , 227, 1-13.Maqrini,H & Teghem,H. (1995). Scheduling complex flexible job shop problems. Proceedings INRIA/IEEE Symposium on Emerging Technologies and Factory Automation. ETFA95, Paris, France, 541-549, doi: 10.1109/ETFA.1995.496806.Marimin, M., & Farhan, M. (2020). Sustainable flexible flow shop scheduling optimization in flexible packaging industry using genetic algorithm. IOP Conf. Series. Earth and Environmental Science, 472, 012050. doi:10.1088/1755-1315/472/.Marimin, Darmawan, M.A., Widhiarti, R.P., & Teniwut, Y.K., 2018. Green productivity improvement and sustainability assessment of the motorcycle tire production process: A case study. J. Clean. Prod. 191, 273–282. https://doi.org/10.1016/j.jclepro.2018.04.228Marqués,M. (2017). Sostenibilidad, comunicación y valor compartido: el discurso actual del desarrollo sostenible en la empresa española (Tesis Doctoral). Madrid: Universidad Complutense de Madrid.Martínez,J. (2017). Aplicación de los instrumentos Brief y Best en la detención de riesgo ergonómico en la industria metalmecánica. Revista Tog, 14(26), 374-385Martínez León, H.C., & Calvo-Amodio, J., 2017. Towards lean for sustainability: Understanding the interrelationships between lean and sustainability from a systems thinking perspective. J. Clean. Prod. 142, 4384–4402. https://doi.org/10.1016/j.jclepro.2016.11.132Martínez,S., Pérez,E., Eguía, P.,Erkorekab,A., & Granada,E. (2020). Model calibration and exergoeconomic optimization with NSGA-II applied to a residential cogeneration. Applied Thermal Engineering, 169, 1-14. Recuperado de https://doi.org/10.1016/j.applthermaleng.2020.114916.May, G., Stahl, B., Taisch, M., & Prabhu, V. (2015). Multi-objective genetic algorithm for energy-efficient job shop scheduling. International Journal of Production Research, 53(23), 7071-7089. Retrieved from http://www.tandfonline.com/doi/abs/10.1080/002.May, G., Stahl, B., Taisch, M., & Kiritsis, D., 2017. Energy management in manufacturing: from literature review to a conceptual framework. J. Clean. Prod. 167, 1464e1489.McCarthy. (1959). Programs With Common Sense. Computer Science Department. Stanford University, 1-15.McCarthy, J. (1956). Dartmouth Summer Research Conference on Artificial Intelligence. Dartmouth College.Meiting,L., & Hua,W. (2020). Angular or rounded? The effect of the shape of green brand logos on consumer perception. Journal of Cleaner Production, 279, 1-7.Mekni, S., & Chaâr, B. (2014). An enhanced PSO-based algorithm for multiobjective flexible job shop scheduling problems(FJSSPs). Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems - MEDES, 14 (12), 178–182. Recuperado de http://doi.org/10.1145/2668260.2668289.Melchio, C., & Henriqson, E. (2020). Systematic Literature Review on Sustainability Metrics. Rev. FSA, 17(2), 24–39. Recuperado de http://www4.fsanet.com.br/revista/index.php/fsa/article/view/1930.Meng,L., Zhang,C. Shao,X., & Ren,Y. (2019). MILP models for energy-aware flexible job shop scheduling problem. Journal of Cleaner Production, 210, 710- 723. Recuperado de https://doi.org/10.1016/j.jclepro.2018.11.021.Min Liu a, Xifan Yao a,∗, Yongxiang Li a,b. (2020). Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems. Applied Soft Computing Journal , 87, 1-16. Recuperado de https://doi.org/10.1016/j.asoc.2019.105954.Min, H., & Choi, S.B. (2019). Green sourcing practices in Korea. Manag. Res. Rev. 43, 1–18. https://doi.org/10.1108/MRR-11-2018-0446Dai,M., Dunbing,T., Giret,A.,& Salido,M. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer Integrated Manufacturing, 59 (2019) 143–157. Recuperado de https://doi.org/10.1016/j.rcim.2019.04.006.Minambiente. (2019). Estudio Nacional del agua. Obtenido de http://documentacion.ideam.gov.co/openbiblio/bvirtual/023858/ENA_2018.pdfMinisterio para la Transición Ecologica y el Reto Demográfico. (2020). Registro de huella de carbono, compensación y proyectos de absorción de dióxido de carbono. España.Minsky,M. (1985). Communication with Alien Intelligence. Byte: Artificial Intelligence, 10(4), 127-138.Minsky,M. (1961). Steps Toward Artificial Intelligence. Proceedings Of The Ire, 8-30.Mohammadi,S., Al-e-Hashem,M., & Rekik,Y. (2020). An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company. International Journal of Production Economics, 219, 347–359. Recuperado de https://doi.org/10.1016/j.ijpe.2019.05.017.Mokhtari, H., & Dadgar, M. (2015). Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate. Computers & Operations Research, 61, 31–45. Recuperado de http://doi.org/10.1016/j.cor.2015.02.014.Mokhtari,H & Hasani,A. (2017). An energy-efficient multi-objective optimization for flexible job-shopscheduling problem. Computers and Chemical Engineering, 104 (2017) 339–352. Recuperado de http://dx.doi.org/10.1016/j.compchemeng.2017.05.004.Moniz,D., Pedro,J., Horta,N., & Pires,J. (2019). Multi-objective framework for cost-effective OTN switch placement using NSGA-II with embedded domain knowledge. Applied Soft Computing Journal, 83, 1-12. recuperado de https://doi.org/10.1016/j.asoc.2019.105608.Montalban,L., García,L., Sanz,A., & Pellicer,E. (2018). Social sustainability criteria in public-work procurement: An international perspective. Journal of Cleaner Production , 198, 1355- 1371. Recuperado de https://doi.org/10.1016/j.jclepro.2018.07.083.Moon, J., & Park, J. (2014). Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage. International Journal of Production Research, 52(13), 3922-3939. Recuperado de http://www.tandfonline.com/doi/abs/10.1080/00207543.2013.860251.Moore,L., Wurzelbacher,S., & Shockey,T. (2018). Workers compensation insurer risk control systems: Opportunities for public health collaborations. Journal of Safety Research, 66, 141–150.Recuperado de https://doi.org/10.1016/j.jsr.2018.07.004.Moradi,S., Fatemi,G., & Zandieh,M. (2011). Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem. Expert Systems with Applications, 38, 7169–7178.Morais,D.,& Silvestre,D. (2018). Advancing social sustainability in supply chain management: Lessons from multiple case studies in an emerging economy. Journal of Cleaner Production, 199, 222-235. Recuperado de https://doi.org/10.1016/j.jclepro.2018.07.097.Moslehi,G., & Mahnam,M. (2011). A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int. J. Production Economics, 12(9), 14–22.Mülling,D., Land,A., Seuring,S., & Machado, L. (2018). Linking sustainability-oriented innovation to supply chain relationship integration. Journal of Cleaner Production, 172, 3448-3458. Recuperado de https://doi.org/10.1016/j.jclepro.2017.11.091.Murphy,Y., Brennan, E., & Flessner,E. (2019). Anxiogenic parenting practices as predictors of pediatric body-focused repetitive behaviors. Journal of Obsessive-Compulsive and Related Disorders , 21, 46–54. Recuperado de https://doi.org/10.1016/j.jocrd.2018.12.002.Muth, J. F., & Thompson, G. L. (1963). Industrial Scheduling, Prentice Hall, Englewood Cliffs, New Jersey, Ch 15, 225-251.Naciones Unidas. (2015). Convención Marco sobre el Cambio Climático. Paris.Ning, T.,& Jin,H. (2018). A cloud based improved method for multi-objective flexible job-shop scheduling problem. Journal of Intelligent and Fuzzy Systems, 35 (12), 823-829.Nitisiria,K., Gena,M., & Ohwadaa,M. (2019). A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling. Computers & Industrial Engineering, 130, 381–394. Recuperado de https://doi.org/10.1016/j.cie.2019.02.035.Norma GT 45. (2015). Instituto Colombiano de Normas Técnicas y Certificación. Guía para la identificación de los peligros y la valoración de los riesgos en seguridad y salud ocupacional. Bogotá: Icontec.Noticias ONU. (2018). El Acuerdo de París sobre cambio climático no es suficiente. Obtenido de https://news.un.org/es/interview/2018/09/1441622Noticias ONU. (2019). Termina la COP25 con pocos avances en cuanto a la reducción de emisiones de carbono. Obtenido de https://news.un.org/es/story/2019/12/1466671Noticias ONU. (2020). El cambio climático es más mortal que el coronavirus. Obtenido de https://news.un.org/es/story/2020/03/1470901Nouiri, M., Bekrar,A., & Trentesaux.D. (2018). Towards Energy Efficient Scheduling and Rescheduling for Dynamic Flexible Job shop problem. IFAC (International Federation of Automatic Control) Papers On Line , 51(11), 1275–1280. doi: 10.1016/j.ifacol.2018.08.357.Nouiri,M., Bekrar,A., Jemai,A., Niar,S., & Ammari,A. (2018a). An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J Intell Manuf, 29, 603–615. Recuperado de https://doi.org/10.1007/s10845-015-1039-3.Nouri,H. (2016). Development of a comprehensive model and BFO algorithm for a dynamic cellular manufacturing system. AppliedMathematicalModelling, 40, 1514–1531. Recuperado de http://dx.doi.org/10.1016/j.apm.2015.09.004.NTC ISO 14001. (2015). Sistemas de Gestión Ambiental. Requisitos con orientación para su uso. Bogotá: Icontec. Instituto Colombiano de Normas Técnicas y Certificación .Ojstersek, R., Lalic, D., Buchmeister, B. (2019). A new method for mathematical and simulation modelling interactivity: A case study in flexible job shop scheduling. Advances in Production Engineering & Management, 14(4), 435–448.Okoshia,C., Limab,E., & Gouvea,E . (2019). Performance cause and effect studies: Analyzing high performance manufacturing companies. International Journal of Production Economics, 210, 27–41. Recuperado de https://doi.org/10.1016/j.ijpe.2019.01.003.Onyeocha, C. (2014). Robust Production & Inventory Control Systems for Multi-product Manufacturing Flow Lines. Universidad de Dublín.Ouoba,Y. (2017). Economic sustainability of the gold mining industry in Burkina Faso. Resources Policy, 51, 194–203. Recuperado de http://dx.doi.org/10.1016/j.resourpol.2017.01.001.Owais,M., & Osman,M. (2018). Complete hierarchical multi-objective genetic algorithm for transit network design problem. Expert Systems With Applications, 114, 143–154.Ozcan,S., & Simsir,F. (2019). A new model based on Artificial Bee Colony algorithm for preventive maintenance with replacement scheduling in continuous production lines. Engineering Science and Technology, an International Journal , 22, 1175–1186.Ozturk,G., Bahadir,O., & Teymourifar,A. (2018). Extracting priority rules for dynamic multiobjective flexible job shop scheduling problems using gene expression programming. International Journal of Production Research, 57(19), 3121–3137.Pagell, M., & Shevchenko, A. (2014). Why research in sustainable supply chain management should have no future. Development of Truly Sustainable Supply Chains, 50(1), 44–55.Paik,S., & Zalk,D. (2019). A Simple Proposition for Improving Industrial Hygiene Air Sampling Methods. Safety and Health at Work , 10, 389- 392. Recuperado de https://doi.org/10.1016/j.shaw.2019.07.001.Pajares,G & Santos,M. (2006). Inteligencia artificial e inteligencia del conocimiento . México: Alfaomega.Palacios, J. J., González, M. A., Vela, C. R., González, I., & Puente, J. (2015). Genetic tabu search for the fuzzy flexible job shop problem. Computers & Operations, 54, 74–89. http://doi.org/10.1016/j.cor.2014.08.023.Pandian, P., Sankar,S., Ponnambalam & Raj,V. (2012). Scheduling of Automated Guided Vehicle and Flexible Jobshop using Jumping Genes Genetic Algorithm. American Journal of Applied Sciences, 9 (10), 1706-1720.Pehrsson, L. (2013). Manufacturing Management And Decision support using simulation-based Multi-objective Optimisation. Suecia: Universidad de Skovde.Pérez,R., & Hernández,A. (2018). A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility. Applied Intelligence, 48:3707–3734. Recuperado de https://doi.org/10.1007/s10489-018-1160-z.Pezzellaa,F., Morgantia, G., Ciaschettib,F. (2008). A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers & Operations Research, 35, 3202 – 3212. doi:10.1016/j.cor.2007.02.014.Pinedo, M. (2005). Planning and scheduling in manufacturing and services. Vol. 24. New York: Springer.Pinedo, M. (2016). Scheduling: Theory, Algorithms and Systems. Fifth Edition. New York: Springer.Pires,J., Ferreira,A.,Bartocci,L., & Pinheiro,D. (2019). Systemic indicator of sustainable development: Proposal and application of a framework. Journal of Cleaner Production 241 (2019) 118383, 241,1-10. Recuperado de https://doi.org/10.1016/j.jclepro.2019.118383.Piroozfard, H., Wong,K., & Peng, W. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling, 128, 267–283. Recuperado de http://dx.doi.org/10.1016/j.resconrec.2016.12.001.PNUD. (2016). Objetivos de Desarrollo Sostenible. Herramientas de aproximación al contexto local. Obtenido de https://www.undp.org/content/dam/colombia/docs/ODM/undp-co-ODSColombiaVSWS-2016.pdfPopovic a,T., Barbosa,A., Kraslawski,A.,& Carvalho,A. (2018). Quantitative indicators for social sustainability assessment of supply. Journal of Cleaner Production, 180, 748- 768. Recuperado de https://doi.org/10.1016/j.jclepro.2018.01.142.Pradhan, M & Panda,G. (2017). Information Combining Schemes for Cooperative Spectrum Sensing: A Survey and Comparative Performance Analysis. Wireless Pers Commun, 94, 685–711. doi: 10.1007/s11277-016-3645-6.Purshouse,R. (2003). On the Evolutionary Optimisation of Many Objectives. The University of Sheffield.Quezada,W., Hernández,G., González,E., Rodríguez,R., & Molina,F. (2018). Gestión de la tecnología y su proceso de transferencia en Pequeñas y Medianas Empresas metalmecánicas del Ecuador. Ingeniería Industrial, 3(8), 303-314. Recuperado de http://www.rii.cujae.edu.cu.Quizá, R. (2004). Optimización multiobjetivo del proceso de torneado. Cuba: Universidad de Matanzas .Rabiee , M., Zandieh & Ramezani. (2012). Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. International Journal of Production Research, 50 (24), 7327–7342.Rahmani, A., Siar, H., Shamshirband, S., Shojafar, M., & Nasir, M. (2015). Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises. Annals of Operat, 229(1), 451-474.Rahmati, S., Zandieh, M., & Yazdani, M. (2012). Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 64(5-8), 915–932. Recuperado de http://doi.org/10.1007/s00170-012-4051-1.Rahmati,S., Ahmadi,A.,& Karimi,B. (2018). Multi-objective evolutionary simulation based optimization mechanism for a novel stochastic reliability centered maintenance problem. Swarm and Evolutionary Computation, 40, 255–271. Recuperado de https://doi.org/10.1016/j.swevo.2018.02.010.Ramachandrana,S.,Jayalal, M., Riyas,A., Jehadeesan, R & Devan,K. (2020). Application of genetic algorithm for optimization of control rods positioning in a fast breeder reactor core. Nuclear Engineering and Design , 361, 1-14. Recuperado de https://doi.org/10.1016/j.nucengdes.2020.110541.Ramanathan, T., & Ting, Y. (2015). Selection of wet digestion methods for metal quantification in hazardous solid wastes. Journal of Environmental Chemical Engineering, 3(3), 1459–1467. Recuperado de http://doi.org/10.1016/j.jece.2015.05.006.Ramicic,M., & Bonarini,A. (2020). Adaptation of learning agents through artificial perception. Adaptive Behavior, 28(2),79-89.Reddy, S., Ratnam, Ch., Rajyalakshmi, G., & Manupati, V.K. (2018). An effective hybrid multi objective evolutionary algorithm for solving realtime event in flexible job shop scheduling problem. Measurement , 114 (20) 78–90. Recuperado de https://doi.org/10.1016/j.measurement.2017.09.022.Rena,W., Wena,J., Hua,Y., & Lib,J. (2020). Maintenance service network redesign for geographically distributed moving assets using NSGA-II in agriculture. Computers and Electronics in Agriculture , 169. Recuperado de https://doi.org/10.1016/j.compag.2019.105170.Renna, P. (2012). Controllable processing time policies for job shop manufacturing system. . The International Journal of Advanced Manufacturing Technology, 67(9-12), 2127–2136. http://doi.org/10.1007/s00170-012-4635-9.Resoloución A/68/970. (2014). Informe del Grupo de Trabajo Abierto de la Asamblea General sobre los Objetivos de Desarrollo Sostenible. Ginebra: Naciones Unidas.Resolución 2734 . (2010). “Por la cual se adoptan los requisitos y evidencias de contribución al desarrollo sostenible del país y se establece el procedimiento para la aprobación nacional de proyectos de reducción de emisiones de gases de efecto invernadero que optan al Mecanismo . Bogotá: Ministerio de Ambiente, Vivienda y Desarrollo Territorial .Pontes,R., Ferreira,A., & Custodio,D. (2020). Application of Hybrid Simulation in production scheduling in job shop systems Modeling and Simulation International. Simulation: Transactions of the Society for, 96(3), 253-268.Rohaninejad, M., Sahraeian, R., & Nouri, B. (2016). Multi-objective optimization of integrated lot-sizing and scheduling problem in flexible job shops. Operations Research, 50(3), 587-609. http://dx.doi.org/10.1051/ro/2015049.Romagnoli,G. (2015). Design and simulation of CONWIP in the complex flexible job shop of a Make-To-Order manufacturing firm. International Journal of Industrial Engineering Computations, 6, 117–134, doi:10.5267/j.ijiec.2014.8.003.Rong-Hwa., Tung-Han,Y. (2017). An effective ant colony optimization algorithm for multi-objectivejob shop scheduling with equal-size lot-splittingRong. Applied Soft Computing, 57, 642–656. Recuperado de http://dx.doi.org/10.1016/j.asoc.2017.04.062.Rossi,A.,& Dini,G. (2007). Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method. Robotics and Computer-Integrated Manufacturing, 23, 503–516. doi:10.1016/j.rcim.2006.06.004.Rosso,F., Ciancio,F.,Dell’Olmo,J., & Salata, F. (2020). Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application. Energy & Buildings , 216, 1-18. Recuperado de https://doi.org/10.1016/j.enbuild.2020.109945.Ruan, J., & Xu, Z. (2016). Constructing environment-friendly return road of metals from e-waste: Combination of physical separation technologies. Renewable and Sustainable Energy Reviews, 54, 745–760. Recuperado de http://doi.org/10.1016/j.rser.2015.10.114.Rubaiee, S., & Yildirim, M.B., 2019. An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling. Comput. Ind. Eng. 127, 240–252. https://doi.org/10.1016/j.cie.2018.12.020Ruiz, M.R., Briceño, L.J., Severiche, C.A., & Durán, L. (2019). Legal framework of environmental management for agricultural SMEs: Caribbean Colombian Context. . Revista Espacios, 40(32), 9-15. Recuperado de https://www.revistaespacios.com/a19v40n32/a19v40n32p09.pdf.Ruiz, S. (2015). Metodología multiobjetivo basada en un comportamiento evolutivo para programar sistemas de producción flexible job shop. Aplicaciones en la industria metalmecánica. Universidad Nacional de Colombia.Russell, S., & Norvig, P. (2004). Inteligencia Artificial: un enfoque moderno. México: Printice Hall.Saad,M., Nazzala,N., & Darrasa,B. (2019). A general framework for sustainability assessment of manufacturing processes. Ecological Indicators, 97, 211–224. Recuperado de https://doi.org/10.1016/j.ecolind.2018.09.062.Sahu,A., Padhy,R., Das,D., & Gautam,A. (2020). Improving financial and environmental performance through MFCA: A SME case study. Journal of Cleaner Production, 123751, 1-21.Sajan, M.P., Shalij, P.R., Ramesh, A., Biju Augustine, P., 2017. Lean manufacturing practices in Indian manufacturing SMEs and their effect on sustainability performance. J. Manuf. Technol. Manag. 28, 772e793. https://doi.org/10.1108/ JMTM-12-2016-0188.Salas,K., Meza,J., Obredor,T., & Mercado,N. (2019). Evaluación de la Cadena de Suministro para Mejorar la Competitividad y Productividad en el Sector Metalmecánico en Barranquilla, Colombia. Información Tecnológica, 30 (2)., 25-32. Recuperado de http://dx.doi.org/10.4067/S0718-07642019000200025.Salazar, E., & Sarzuri, R. (2015). Algoritmo genético mejorado para la minimización de la tardanza total en un Flowshop flexible con tiempos de preparación dependientes de la secuencia. Revista Chilena de Ingeniería, 23(1), 118-127.Sangwa,N., & Sangwan,K. (2018). Development of an integrated performance measurement framework for lean organizations. Journal of Manufacturing Technology Management, 29(1).doi:10.1108/JMTM-06-2017-0098.Sarache, W., Cárdenas, D., Giraldo J., & Parra J. (2007). Procedimiento para evaluar la estrategia de manufactura: aplicaciones en la industria metalmecánica. Pontificia Universidad Javeriana, Facultad de Ciencias Económicas y Administrativas.Sarache, W., Cárdenas, D., Castrillón, O., Becerra, F., García, A., Giraldo, J., Ibarra, S., Ruiz, S., Tamayo, J., & Zapata, A. (2008). Gestión de la producción: Una aproximación conceptual. Unibiblios, Bogotá.Sarangi,G., Mishrab, A., Changc,Y., & Taghizadeh,F. (2019). Indian electricity sector, energy security and sustainability: An empirical assessment. Energy Policy, 135, 1-15. Recuperado de https://doi.org/10.1016/j.enpol.2019.110964.Scassellati,B., Brawer,J., Tsui,K., Gilani,s., Malzkuhn,M., Manini,B., & Stone,K . (2018). Teaching Language to Deaf Infants with a Robot and a Virtual Human. CHI, 21–26, doi: https://doi.org/10.1145/3173574.3174127.Schaffer, J. (1985). Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the 1st International Conference on Genetic Algorithms, Pittsburgh, PA, USA, 93-100. Recuperado de https://doi.org/10.1016/0066-4138(85)90462-8.Segersted,E.,& Abrahamsson,L. (2019). Diversity of livelihoods and social sustainability in established mining communities. The Extractive Industries and Society , 6, 610–619. Recuperado de https://doi.org/10.1016/j.exis.2019.03.008.Selander,J., Rylander,L.,Albin,M., Rosenhall,U.,Lewné,M., & Gustavsson. (2019). Full-time exposure to occupational noise during pregnancy was associated with reduced birth weight in a nationwide cohort study of Swedish women. Science of the Total Environmen, 1137–1143. https://doi.org/10.1016/j.scitotenv.2018.09.212. SENA. (2012). Servicio Nacional de Aprendizaje. Caracterización del sector metalmecánico y área de soldadura. Bogotá.SENA. (2013). Servicio Nacional de Aprendizaje. Sector metalmecánico: retos de cara al futuro. Obtenido de http://periodico.sena.edu.co/productividad/noticia.php?t=sector-metalmecanico-retos-de-cara-al-futuro&i=868Sethi, P., Chakrabarti,D., & Hattacharjee,S. (2020). Globalization, financial development and economic growth: Perils on the environmental sustainability of an emerging economy. Journal of Policy Modeling, Recuperado de https://doi.org/10.1016/j.jpolmod.2020.01.007.Shaheen,A., Spea, S., Farrag,S., & Abido,M. (2018). A review of meta-heuristic algorithms for reactive power planning problem. Ain Shams Engineering Journal , 9, 215–231. Recuperado de http://dx.doi.org/10.1016/j.asej.2015.12.003.Shahsavari, P. N., & Ghasemishabankareh, B. (2013). A novel hybrid meta-heuristicalgorithm for solving multi objective flexible job shop scheduling. Journal of Manufacturing Systems, 32(4), 771–780.Shao, X., Liu, W., Liu, Q., & Zhang, C. (2013). Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 67(9-12), 2885–2901. Recuperado de http://doi.org/10.1007/s00170-012-4701-3.Shen,X., & Yao, X. (2015). Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems. Information Sciences, 298, 198–224. recuperado de http://doi.org/10.1016/j.ins.2014.11.036.Shen,L., Dauzère,S., Neufeld,D. (2018). Solving the flexible job shop scheduling problem with sequence-dependent setup times. European Journal of Operational Research , 265, 503–516. Recuperado de http://dx.doi.org/10.1016/j.ejor.2017.08.021.Shen,X., Han,J., & Fu,J. (2017). Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems. Soft Comput, 21, 6531–6554. doi: 10.1007/s00500-016-2245-4.Shi, J., Jiao, H., & Chen, T. (2012). Multi-objective pareto optimization on flexible job-shop scheduling problem about due punishment. Journal of Mechanical Engineering, 48(12), 184. Recuperado de https://scholar.google.es/scholar?q=Multi-objective+pareto+optimization+on+flexible+jobshop+scheduling+problem+about+due+punishment++&btnG=&hl=en&as_sdt=0%2C5#0.Shiel,A., Paço,A., & Alves,H. (2020). Generativity, sustainable development and green consumer behaviour. Journal of Cleaner Production , 245,1-9. Recuperado de https://doi.org/10.1016/j.jclepro.2019.118865 .Shivasankaran, N. (2015). Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling. International Journal of Computational Intelligence Systems, , 8(3), 455-466. Recuperado de http://www.tandfonline.com/doi/abs/10.1080/18756891.2015.1017383.Shoham,Y. (1997). An Overview of Agent-Oriented Programming. En J. Bradshaw, Software Agents (págs. 271-290). Cambridge, Massachusetts: AAAI Press/The MIT Press.Shokouhi,E. (2018). Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints. Production & Manufacturing Research, 6 (1), 61-89, Recuperado de https://doi.org/10.1080/21693277.2017.1415173.Silva,P., Tavares,T., Botelhoa, Oliveira,P., Schaffert,P., Costa,R., & Rodrigues,J. (2020). Intrapopulation recurrent selection in sweet sorghum for improving sugar. Industrial Crops & Products, 143(2), Recuperado de https://doi.org/10.1016/j.indcrop.2019.111910.Silva,R., Rab,L., Rodrigues,J.,Solic,P., & Carvalho,A. (2020). A preference-based demand response mechanism for energy management in a microgrid. Journal of Cleaner Production, 255 (11),1-14. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120034.Singh, A. (2013). Minimization of mean tardiness in a flexible job shop. International Journal of Simulation Modelling, 3, 190-204. Recuperado de http://ww.w.ijsimm.com/Full_Papers/Fulltext2013/text12-3_190-204.pdf.Singh, M., Singh, M., Mahapatra, S., & Jagadev, N. (2016). Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem. International Journal of Advanced Manufacturing Technology, 85(9-12), 2353-2366. doi: 10.1007/s00170-015-8075-1.Singh, A.P., & Dhadse, K., 2021. Economic evaluation of crop production in the Ganges region under climate change: A sustainable policy framework. J. Clean. Prod. 278, 123413. https://doi.org/10.1016/j.jclepro.2020.123413Siqueira,C., SouzabS.R. & de Souza,C. (2018). A Multi-objective Variable Neighborhood Search algorithm for solving the Hybrid Flow Shop Problem. Electronic Notes in Discrete Mathematics, 66, 87-94. https://doi.org/10.1016/j.endm.2018.03.012.Sitcharangsie,S., Ijomah,W., & Wong,T. (2019). Decision makings in key remanufacturing activities to optimise remanufacturing outcomes: A review. Journal of Cleaner Production, 232, 1465- 1481. Recuperado de https://doi.org/10.1016/j.jclepro.2019.05.204.Srinivas,N & Kalyanmoy,D. (1995). Muiltiobj ective Optimization using nondominated sorting in Genetic Algorithms. Evolutionary Computation , 2(3), 22 1-248.Staniskienhé, E., & Stankeviciuté, Z. (2018). Social sustainability measurement framework: The case of employee perspective in a CSR-committed organisation. Journal of Cleaner Production, 188, 708-719. Recuperado de https://doi.org/10.1016/j.jclepro.2018.03.269.Steensels,J., Gallone,B.,Voordeckers,K.,& Verstrepen,K . (2020). Domestication of Industrial Microbes. Current Biology, https://doi.org/10.1016/j.cub.2019.04.025.Steuer, R., Choo, E. (1983). An interactive weighted tchebychev procedure for multiple objective programming. Mathematical Programming, 26, 326-344. doi:10.1007/BF02591870C.Suman,G., & Prajapati,D. (2018). Control chart applications in healthcare: a literature review. International Journal of Metrology and Quality Engineering, 9(5),1-21. Recuperado de https://doi.org/10.1051/ijmqe/2018003.Sun,J., & Xu,L. (2019). Disruption management of multi-objective flexible job shop scheduling problem. Academic Journal Of Manufacturing Engineering, 11(2), 50-56.Sun,X., Guo,S., & Du,B. (2019). A Hybrid Multi-Objective Evolutionary Algorithm With Heuristic Adjustment Strategies and Variable Neighbor-Hood Search for Flexible Job-Shop Scheduling Problem Considering Flexible Rest Time. IEEE Access, (7), 157003 - 157018.Suthar,H., & Gadit,J. (2019). Multiobjective optimization of 2DOF controller using Evolutionary and Swarm intelligence enhanced with TOPSIS. Heliyon, 5(9), 2-28, doi: 10.1016/j.heliyon.2019.Syafaruddin. (2019). Review on Multi-Objectives Optimization Methods in Hybrid Power Generation. Journal of Engineering Science and Technology Review 12 (1) (2019) 143- 152, 12(1)143 -152. doi:10.25103/jestr.121.17.Ta, V., Griffith,C., Boatfield,C., Wang,X., Civitello,M., Bader, H & Loggarakis,A. (2020). User Experiences of Social Support From Companion Chatbots in Everyday Contexts: Thematic Analysis. Journal Of Medical Internet Research, 22,1-11. Recuperado de http://www.jmir.org/2020/2/e16235/.Tadros,A., Sharon,M., Chill,N., Dragan,M., Rowell,J., & Hoffman,S. (2018). Emergency department visits for work-related injuries. American Journal of Emergency Medicine , 36, 1455–1458. Recuperado de https://doi.org/10.1016/j.ajem.2018.04.058.Tamayo, J., Romero,E., Gamero,J., & Martínez,J. (2015). Do Innovation and Cooperation Influence SMEs Competitiveness? Evidence From the Andalusian Metal-Mechanic Sector. Innovar, 25(55), 101-115. recuperado de http://dx.doi.org/10.15446/innovar.v25n55.47226.Tamerabeta,Y., Adjadja,T ., & Bentrciaa. (2018). Evaluation of the genetic algorithm performance for the optimization of the grand potential in the cluster variation method. Calphad , 61, 157–164. Recuperado de https://doi.org/10.1016/j.calphad.2018.03.007.Tanabe,K. (2018). Pareto’s 80/20 rule and the Gaussian distribution. Physica, 510, 635–640. Recuperado de https://doi.org/10.1016/j.physa.2018.07.023.Tang, H., Chen, R., Li,Y., Z., Guo, P., & Du,Y. (2019). Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop. Applied Soft Computing Journal, 78, (21), 176–194. Recuperado https://doi.org/10.1016/j.asoc.2019.02.011.Tay, J. C., & Ho, N. (2008). Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering, 54(3), 453–473. recuperado de http://doi.org/10.1016/j.cie.2007.08.008.Tian,N; & Ji,Z. (2015). Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization. Hindawi Publishing Corporation, 7(9),1-11. Recuperado de http://dx.doi.org/10.1155/2015/940592.Tian,S., Wang,T., Zhang,H., & 4, Wu,H. (2019). An Energy-Efficient Scheduling Approach for Flexible Job Shop Problem in an Internet of Manufacturing Things Environment. IEEEE Access. Special section on green communications on wireless networks, 8(11), 62695- 62704.Trujillo, M. (2018). Indicador de desempeño ambiental bajo el enfoque GSCM: Validación en empresas manufactureras de la región del eje cafetero. Universidad Nacional de Colombia.Vargas,Y. (2018). Perfil de salud laboral en Colombia a partir del análisis y caracterización de la enfermedad laboral reportada en el Sistema General de Riesgos Laborales. Periodo 2004 – 2014. Bogotá: Universidad Nacional de Colombia.Velter,M., Bitzer,V., Bocken,N., & Kemp,R. (2020). Sustainable business model innovation: The role of boundary work for multi-stakeholder alignment. Journal of Cleaner Production , 247, 1-17. Recuperado de https://doi.org/10.1016/j.jclepro.2019.119497.Venjakob,C., 1,2, Klein,A., Ebeling,A., Tscharntke,T., & Scherbe,C. (2016). Plant diversity increases spatio-temporal niche complementarity in plant-pollinator interactions. Ecology and Evolution, 6(8): 2249–2261.doi: 10.1002/ece3.2026.Villeneuve,C., Tremblay,D., Riffon,O., Lanmafankpotin,G.,& Bouchard,S. (2017). A Systemic Tool and Process for Sustainability Assessment. Sustainability, 9-19. doi:10.3390/su9101909.Vital,A., Azaba,A.,& Fazle Bakib,M. (2020). Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility. Journal of Manufacturing Systems , 54, 74–93. Recuperado de https://doi.org/10.1016/j.jmsy.2019.11.010.Wang, C., Ji, Z.,& Wang,Y. (2017). A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem. Mathematical Problems in Engineering, 1-21. Recuperado de https://doi.org/10.1155/2017/2857564.Wang, G., Li, C., & Cui, H. (2013a). Simulation Optimization of Multi-Objective Flexible Job Shop Scheduling. Applied Mechanics and Materials, 365(5), 602-605. Recuperado de http://www.scientific.net/AMM.365-366.602.Wang, S., Liu, C., Pei, D., & Wang, J. (2013b). A novel hybrid election campaign optimisation algorithm for multi-objective flexible job-shop scheduling problem. International Journal of Materials and Structural Integrity, 7(1/2/3), 160. Recuperado de http://doi.org/10.1504/IJMSI.2013.055113.Wang, W., Fan, L., Xu, X., Zhao, Y., & Zhang, J. (2013c). Multi-objective differential evolution algorithm for flexible Job-Shop batch scheduling problem. Computer Integrated Manufacturing Systems, http://en.cnki.com.cn/Article_en/CJFDTOTAL-JSJJ201310013.htm19(10), 2481–2492. Recuperado de.Wang, X., Gao, L., Zhang, C., & Shao, X. (2010a). A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. International Journal of Advanced Manufacturing Technology, 51, 757-767. doi: 10.1007/s00170-010-2642-2. doi: 10.1007/s00170-013-4923-z.Wang, X., Li, W., & Zhang, Y. (2013d). An improved multi–objective genetic algorithm for fuzzy flexible job–shop scheduling problem. International Journal of Computer, 47(2/3), 280-288.Wang, Y., Feng, Y., Tan, J., Gao, Y. (2011). Multi-objective optimization method of flexible job-shop lot-splitting scheduling. Journal of Zhejiang University (Engineering Science), 45(4), 719-726.Wang,J., Yang,J.,Zhang,Y., Ren,S & Liu,Y. (2020). Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods. Journal of Cleaner Production, 247, Recuperado de https://doi.org/10.1016/j.jclepro.2019.119093.Wang,J., Zhang,B., Li, Lin., Bai, D., & Feng, Y. (2020a). Due-window assignment scheduling problems with position-dependent weights on a single machine, Engineering Optimization, 52:2, 185-193, DOI: 10.1080/0305215X.2019.1577411Wang,L., Zhou, G., Xu,Y., & Min,L. (2012). An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling. Int J Adv Manuf Technol , 11(60), 1111–1123.doi: 10.1007/s00170-011-3665-z.Wang,Y.,& Shawb,D. (2018). The complexity of high-density neighbourhood development in China: Intensification, deregulation and social sustainability challenges. Sustainable Cities and Society, 43, 578–586. Recuperado de https://doi.org/10.1016/j.scs.2018.08.024.Wassenhove,L.,& Gelders,L. (1980). Solving a bicriterion scheduling problem. European Journal of Operational Research, 4 (1), 42-48. Recuperado de https://doi.org/10.1016/0377-2217(80)90038-7Get rights and content.Wolowski,L., Carvalheiro,L.,& Freitas,L. (2017). Influence of plant–pollinator interactions on the assembly of plant and hummingbird communities. Journal of Ecology 2017, 105, 332–344. doi: 10.1111/1365-2745.12684.Woodcraft, S. (2012). Social sustainability and new communities: Moving from concept to practice in the UK. Procedia-Social and Behavioral Sciences, 68, 29–42. Recuperado de http://doi.org/10.1016/j.sbspro.2012.12.204.Wouters,M.,& Stecher,J. (2017). Development of real-time product cost measurement: A case study in a medium-sized manufacturing company. Int. J. Production Economics, 183, 235–244. Recuperado de http://dx.doi.org/10.1016/j.ijpe.2016.10.018.Wu,R., Li,Y., Guo,S., & Li,X. (2018). An Efficient Meta-Heuristic for Multi-Objective Flexible Job Shop Inverse Scheduling Problem. IEEE Access, 59515- 59527.Wu,X.,& Sun,Y. (2018). A green scheduling algorithm for flexible job shop with energy-saving. Journal of Cleaner Production, 3249- 3264. Recuperado de https://doi.org/10.1016/j.jclepro.2017.10.342.Wu,W., Shen, X & Lib,C. (2019). The flexible job-shop scheduling problem considering deterioration effect and energy consumption simultaneously. Computers & Industrial Engineering , 135, 1004–1024. Recuperado de https://doi.org/10.1016/j.cie.2019.06.048.Xia,W & Zu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48, 409–425. doi:10.1016/j.cie.2005.01.018.Xie,J., Gao,L., Peng,L.,Li,X., & Li,H. (2019). Review on flexible job shop scheduling. IET Collaborative Intelligent Manufacturing, 1(3), 67-77. doi: 10.1049/iet-cim.2018.0009.Xing, L., Chen, Y., & Yang, K . (2008). Double Layer ACO Algorithm for the Multi-Objective FJSSP. New Generation Computing, 26(4), 313–327. Recuperado de http://doi.org/10.1007/s00354-008-0048-6.Xing,L., Chen,Y., & Yang,K. (2009a). Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling. Applied Soft Computing, 9(11), 362–376.Xing,L., Chen,Y.,& Yang,K. (2009b). An efficient search method for multi-objective flexible job shop. J Intell Manuf, 20, 283-293. doi: 10.1007/s10845-008-0216-z.Xiong, J., Tan, X., Yang, K., Xing, L., & Chen, Y. (2012). A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems. . Mathematical problems in engineering, 12(33), 12-27p. doi:10.1155/2012/478981.Xiong, J., Xing, L., & Chen, Y. (2013). Robust scheduling for Multi-objective Flexible Job Shop problems with random machine breakdowns. International Journal of Production Economics, 141(1), 112–126. Recuperado de http://www.sciencedirect.com/science/article/pii/S0925527310001739.Yang, Y., Huang, M.,Wang, Z.,Zhu, Q. (2018). Solving Bi-objective FJSP Using Limited Stable Matching Strategy. China Mechanical Engineering, 29(14), 1743-1750.Yang,Y., Zeng,Z., Wang,R & Sun,X. (2016). Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times. journal One, 11(12), doi:10.1371/journal.pone.0167427.Ye, J., & Ma, H. (2015). Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem. Mathematical Problems in Engineering, 4(11),1-9. Recuperado de http://www.hindawi.com/journals/mpe/2015/725460/abs/.Yi,J., Xing,L., Wang, G.,Dong,J., Vasilakos,A., & Alavi, A. (2020). Behavior of crossover operators in NSGA-III for large-scale optimization problems. Information Sciences , 509, 470–487. Recuperado de https://doi.org/10.1016/j.ins.2018.10.005.Yilmaz, O .,& Durmusolgu,C. (2018). A performance comparison and evaluation of metaheuristics for a batch scheduling problem in a multi-hybrid cell manufacturing system with skilled workforce assignment. Journal of Industrial and Management Optimization, 14(1), 1219-1249.Yin,L., Li,X., Gao,L., Lu,C.,& Zhang,Z. (2017). A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem. Sustainable Computing: Informatics and Systems, 13, 15–30. Recuperado de http://dx.doi.org/10.1016/j.suscom.2016.11.002.Yu, W., & Shi,X. (2019). An intelligence evaluation method of the environmental impact for the cutting process. Journal of Cleaner Production , 227, 229- 236. Recuperado de https://doi.org/10.1016/j.jclepro.2019.03.336.Yuan, K., Zhu, J., Ju,Q., & Wang, Y. . (2006). Integrated operator genetic algorithm for solving multi-objective flexible job-shop scheduling. Transactions of Nanjing University of Aeronautics and Astronautics, 278-282.Yuan, Y., & Xu, H. (2015). Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms. Transactions on Automation Science and Engineering, 12(1), 336–353. Recuperado de http://doi.org/10.1109/TASE.2013.2274517.Yuan,J., Chen,K., Li,W., Ji,C., Wang,Z., & Skibniewski,M. (2018). Social network analysis for social risks of construction projects in high-density urban areas in China. Journal of Cleaner Production, 198, 940- 961. Recuperado de https://doi.org/10.1016/j.jclepro.2018.07.109.Zaman,F., Elsayed,S., Sarker,R., Essam, D., & Coello,C. (2021). An evolutionary approach for resource constrained project scheduling with uncertain changes. Computers and Operations Research 125 (2021) 105104, 125, 105104 - 105104.Zapata Gomez, A. (2014). La gestión ambiental en el sector empresarial, una visión bajo el enfoque Empresa-Entorno como estrategia de competitividad. Bpgotá: Universidad Nacional de Colombia.Zeleny, M. (1973). Compromise Programming. in Multiple Criteria Decision Making. Columbia, USA: University of South Carolina Press.Zeng, Q., Yang,Y., Shen, L., Zhang, J. (2011). Multiobjective optimization for batch production FJSP based on just in time delivery. Computer Integrated Manufacturing Systems, 17(8),1780-1789.Zhang, C.,Dong, X., Wang, X.,Li,X., & Liu,Q. (2010). Improved NSGA-II for the multi-objective flexible job-shop scheduling problem. Journal of Mechanical Engineering, 46(11), 156-16.Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial Engineering, 56(4),1309–1318. Recuperado de http://doi.org/10.1016/j.cie.2008.07.0.Zhang,H., Ge,H., Pan,R & Wu,Y. (2018a). Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout. Algorithms, 11, 210. doi:10.3390/a11120210.Zhang,K., Shen,C., He,J., & Yen,G. (2020). Knee based multimodal multi-objective evolutionary algorithm for decision making. Information Sciences, 544, 1- 17.Zhang,L., Tang,Q., Wu,Z., & Wang,F. (2017a). Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops. Energy, 138, 210-227. Recuperado de http://dx.doi.org/10.1016/j.energy.2017.07.005.Zhang,Y., Wang,J.,& Liu,Y. (2017b). Game theory based real-time multi-objective flexible job shop scheduling considering environmental impact. Journal of Cleaner Production, 16, 665-679. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2017.08.068.Zhang,Z., Wu,L., Peng,T., & Shun,J . (2018b). An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment. Sustainability, 11(179), 1-21. doi:10.3390/su11010179.Zheng, Y., Li, Y., & Lei, D. (2012). Multi-objective swarm-based neighborhood search for fuzzy flexible job shop scheduling. International Journal of Advanced Manufacturing Technology, 60 (9), 1063-1069. DOI: 10.1007/s00170-011-3646-2Zhenwen,D. (2019). Application of multi-objective memetic algorithm in multi-objective flexible job-shop scheduling problem. Academic Journal of Manufacturing Engineering , 17(3), 24-28.Zhou,B., & Liao,X. (2020). Particle filter and Levy flight-based decomposed multi-objective evolution hybridized particle swarm for flexible job shop greening scheduling with crane transportation. Applied Soft Computing Journal, 91, 1-18.Zhou,X., & Peng,T. (2020). Application of multi-sensor fuzzy information fusion algorithm in industrial. Safety Science , 122,1-5. Recuperado de https://doi.org/10.1016/j.ssci.2019.104531.Zhou,B., Liu,B., Yang, D., Cao,D., & Littler,T. (2020). Multi-objective optimal operation of coastal hydro-electrical energy system with seawater reverse osmosis desalination based on constrained NSGA-III. Energy Conversion and Management, 207, 1- 15. Recuperado de https://doi.org/10.1016/j.enconman.2020.112533.Zhu, C., Qiu, W., Zhu, M., & Chen, G. (2016). Multi-objective flexible job shops robust scheduling problem under stochastic processing times. China Mechanical Engineering Magazine Office, 27, 1667-1672. doi: 10.3969/j.issn.1004-132X.2016.12.019.Zhu, G., & Xu, W. (2019). Multi-objective flexible job shop scheduling method for machine tool component production line considering energy consumption and quality. Control and Decision, 34(2), 252-260.Zhu,W. (2017). Multi-objective dynamic scheduling algorithm for flexible job-shop problem based on rule orientation. System Engineering Theory and Practice, 37(1), 2690-2699.Zhu,Z., & Zhou,C. (2020). An efficient evolutionary grey wolf optimizer for multi-objective flexible job shop scheduling problem with hierarchical job precedence constraints. Computers & Industrial Engineering, 140, 1-16.Zhu,H.,Hea,B., & Lib,H. (2017). Modified Bat Algorithm for the Multi-Objective Flexible Job Shop Scheduling Problem. International Lournal of Performability Endineering, 13 (7), 999-1012, doi: 10.23940/ijpe.17.07.p1.9991012.Zitzler, E., Laumanns,M & Thiele,L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm. ETH Zentrum. Research Collection, 1-22. Recuperado de https://doi.org/10.3929/ethz-a-004284029.Zitzler,C., Laumanns,M., & Thiele,L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Computer Engineering and Networks Laboratory (TIK), 1-23.Zitzler,E., & Thiele,L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257-271. doi: doi: 10.1109/4235.797969.Zitzler,E., Deb,K., & Thiele,L. (2000). Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2): 173-195, 8(2), 173-195.Zou,S., Yaod, X., Zhongd,C., Zhaod, T.,& Huanga,H. (2019). Effectiveness of recurrent selection in Akebia trifoliata (Lardizabalaceae) breeding. Scientia Horticulturae 246 (2019) 79–85, 246, 79–85. 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