Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados

Introducción: El problema Job Shop Con Operadores Calificados o Job Shop With Skilled Operators (JSSO) es una extensión del problema clásico Job Shop en el cual, una operación debe ser ejecutada por un conjunto limitados de trabajadores, con el objetivo de minimizar el tiempo de terminación total de...

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Autores:: López Martínez, César Andrés
Hernández Riaño, Helman Enrique
Soto de la Vega, Manuel Jesús

Tipo de recurso:: Article of journal

Fecha de publicación:: 2019

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: spa

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dc.title.spa.fl_str_mv	Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados
dc.title.translated.eng.fl_str_mv	A Discrete Squirrel Search Algorithm applied to the Job Shop problem with skilled operators
title	Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados
spellingShingle	Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados Combinatorial Optimization swarm intelligence scheduling with operators smallest position value valid particle generator combinatorial optimization inteligencia de enjambres secuenciación con operadores posición del valor más pequeño generador válido de partículas ptimización combinatoria optimización combinatoria
title_short	Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados
title_full	Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados
title_fullStr	Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados
title_full_unstemmed	Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados
title_sort	Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados
dc.creator.fl_str_mv	López Martínez, César Andrés Hernández Riaño, Helman Enrique Soto de la Vega, Manuel Jesús
dc.contributor.author.spa.fl_str_mv	López Martínez, César Andrés Hernández Riaño, Helman Enrique Soto de la Vega, Manuel Jesús
dc.subject.eng.fl_str_mv	Combinatorial Optimization swarm intelligence scheduling with operators smallest position value valid particle generator combinatorial optimization
topic	Combinatorial Optimization swarm intelligence scheduling with operators smallest position value valid particle generator combinatorial optimization inteligencia de enjambres secuenciación con operadores posición del valor más pequeño generador válido de partículas ptimización combinatoria optimización combinatoria
dc.subject.spa.fl_str_mv	inteligencia de enjambres secuenciación con operadores posición del valor más pequeño generador válido de partículas ptimización combinatoria optimización combinatoria
description	Introducción: El problema Job Shop Con Operadores Calificados o Job Shop With Skilled Operators (JSSO) es una extensión del problema clásico Job Shop en el cual, una operación debe ser ejecutada por un conjunto limitados de trabajadores, con el objetivo de minimizar el tiempo de terminación total de los trabajos o Makespan, situación que puede representar distintas aplicaciones en la vida cotidiana. Es un problema complejo y es catalogado como NP-HARD. Objetivo: En este artículo, se aborda el problema JSSO desde la adaptación de un algoritmo conocido como Squirrel Search Algorithm (SSA). Metodología: Se propone un esquema de codificación discreto para el algoritmo SSA utilizando el método Smallest Position Value (SPV). Además, para evitar soluciones que violen las relaciones de precedencia; se corrigen con el método Valid Particle Generator (VPG), el cual garantiza soluciones factibles. Dos versiones del algoritmo se colocan a prueba en 28 instancias propuesta en la literatura para validar su rendimiento. Resultados: Los experimentos computacionales realizados muestran que los dos algoritmos propuestos alcanzan soluciones óptimas en 25 de las 28 instancias analizadas. Además, para las instancias en donde no se logró soluciones óptimas, el gap promedio no supera el 2% para ambas versiones de los algoritmos propuestos. Conclusiones: El esquema de codificación propuesto garantiza la discretización del algoritmo, generando soluciones que convergen hacia el óptimo. Además, la codificación propuesta, permite utilizar de manera natural los operadores de movimiento propuestos originalmente para el algoritmo utilizado. El rendimiento obtenido por los algoritmos es adecuado y de alta calidad.
publishDate	2019
dc.date.accessioned.none.fl_str_mv	2019-07-01 00:00:00 2024-04-09T20:17:36Z
dc.date.available.none.fl_str_mv	2019-07-01 00:00:00 2024-04-09T20:17:36Z
dc.date.issued.none.fl_str_mv	2019-07-01
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dc.relation.references.spa.fl_str_mv	A. Agnetis, G. Murgia and S. Sbrilli, “A job shop scheduling problem with human operators in handicraft production,” Int. J. Prod. Res., vol. 52, no. 13, pp. 3820–3831, Jul. 2014. https://doi.org/10.1080/00207543.2013.831220 M. Abdel-Basset, L. Abdel-Fatah and A. K. Sangaiah, “Metaheuristic Algorithms: A Comprehensive Review,” in Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications. Cambridge, MA, USA: Elsevier Inc., 2018, pp. 185–231. https://doi.org/10.1016/B978-0-12-813314-9.00010-4 M. Jain, V. Singh and A. Rani, “A novel nature-inspired algorithm for optimization: Squirrel search algorithm,” Swarm Evol. Comput., vol. 44, pp. 148–175, Feb. 2019. https://doi.org/10.1016/j.swevo.2018.02.013 L. Gao, X. Li, X. Wen, C. Lu and F. Wen, “A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem,” Comput. Ind. Eng., vol. 88, pp. 417–429, Oct. 2015. https://doi.org/10.1016/j.cie.2015.08.002 K. Z. Gao, P. N. Suganthan, T. J. Chua, C. S. Chong, T. X. Cai and Q. K. Pan, “A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion,” Expert Syst. Appl., vol. 42, no. 21, pp. 7652–7663, Nov. 2015. https://doi.org/10.1016/j.eswa.2015.06.004 K. Z. Gao, P. N. Suganthan, Q. K. Pan, T. J. Chua, C. S. Chong and T. X. Cai, “An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time,” Expert Syst. Appl., vol. 65, pp. 52–67, Dec. 2016. https://doi.org/10.1016/j.eswa.2016.07.046 A. Maroosi, R. C. Muniyandi, E. Sundararajan and A. M. Zin, “A parallel membrane inspired harmony search for optimization problems: A case study based on a flexible job shop scheduling problem,” Appl. Soft Comput. J., vol. 49, pp. 120–136, Dec. 2016. https://doi.org/10.1016/j.asoc.2016.08.007 A. Ahmadi-Javid and P. Hooshangi-Tabrizi, “Integrating employee timetabling with scheduling of machines and transporters in a job-shop environment: A mathematical formulation and an Anarchic Society Optimization algorithm,” Comput. Oper. Res., vol. 84, pp. 73–91, Aug. 2017. https://doi.org/10.1016/j.cor.2016.11.017 H. Piroozfard, K. Y. Wong and A. D. Asl, “An improved biogeography-based optimization for achieving optimal job shop scheduling solutions,” Procedia Comput. Sci., vol. 115, pp. 30–38, Aug. 2017. https://doi.org/10.1016/j.procs.2017.09.073 N. Sharma, H. Sharma and A. Sharma, “Beer froth artificial bee colony algorithm for job-shop scheduling problem,” Appl. Soft Comput. J., vol. 68, pp. 507–524, Jul. 2018. https://doi.org/10.1016/j.asoc.2018.04.001 B. Marzouki, O. B. Driss and K. Ghédira, “Solving Distributed and Flexible Job shop Scheduling Problem using a Chemical Reaction Optimization metaheuristic,” Procedia Comput. Sci., vol. 126, pp. 1424–1433, Jan. 2018. https://doi.org/10.1016/j.procs.2018.08.114 A. Agnetis, M. Flamini, G. Nicosia and A. Pacifici, “A job-shop problem with one additional resource type,” J. Sched., vol. 14, no. 3, pp. 225–237, Jun. 2011. https://doi.org/10.1007/s10951-010-0162-4 M. R. Sierra, C. Mencía and R. Varela, “New schedule generation schemes for the job-shop problem with operators,” J. Intell. Manuf., vol. 26, no. 3, pp. 511–525, Jul. 2013. https://doi.org/10.1007/s10845-013-0810-6 B. Giffler and G. L. Thompson, “Algorithms for Solving Production-Scheduling Problems,” Oper. Res., vol. 8, no. 4, pp. 487–503, Aug. 1960. https://doi.org/10.1287/opre.8.4.487 R. Mencia, M. R. Sierra, C. Mencia and R. Varela, “Genetic Algorithm for Job-Shop Scheduling with Operators,” in New Challenges on Bioinspired Applications, 4th International Work-conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spn, 30 May. - 3 Jun. 2011, pp. 305–314. https://doi.org/10.1007/978-3-642-21326-7 R. Mencía, M. R. Sierra, C. Mencía and R. Varela, “A genetic algorithm for job-shop scheduling with operators enhanced by weak Lamarckian evolution and search space narrowing,” Nat. Comput., vol. 13, no. 2, pp. 179–192, May. 2013. https://doi.org/10.1007/s11047-013-9373-x F. Barber, J. Escamilla, C. Mencia, M. Rodriguez-Molins, M. A. Salido and M. R. Sierra, “Robust solutions to job-shop scheduling problems with operators,” Proc. - Int. Conf. Tools with Artif. Intell. ICTAI, Athens, Greece, 7-9 Nov. 2012. https://doi.org/10.1109/ICTAI.2012.48 M. F. Tasgetiren, M. Sevkli, Y.-Ch. Liang and G. Gencyilmaz, “Particle swarm optimization algorithm for single machine total weighted tardiness problem,” Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), Portland, OR, USA, USA, 19-23 Jun. 2004, pp. 1412–1419. https://doi.org/10.1109/CEC.2004.1331062 R. Chaudhry, S. Tapaswi and N. Kumar, “Forwarding Zone enabled PSO routing with Network lifetime maximization in MANET,” Appl. Intell., vol. 48, no. 9, pp. 3053–3080, Sept. 2018. https://doi.org/10.1007/s10489-017-1127-5 I. Dubey and M. Gupta, “Uniform mutation and SPV rule based optimized PSO algorithm for TSP problem,” Proc. 2017 4th Int. Conf. Electron. Commun. Syst., ICECS 2017, Coimbatore, India, 24-25 Feb. 2017, pp. 168–172. https://doi.org/10.1109/ECS.2017.8067862 N. Kumar and D. P. Vidyarthi, “A model for resource-constrained project scheduling using adaptive PSO,” Soft Comput., vol. 20, no. 4, pp. 1565–1580, Feb. 2015. https://doi.org/10.1007/s00500-015-1606-8 R. L. Graham, E. L. Lawler, J. K. Lenstra and A. H. G. R. Kan, “Optimization and approximation in deterministic machine scheduling: a survey,” Ann. Discret. Math., vol. 5, pp. 287–326, 1979. https://doi.org/10.1016/S0167-5060(08)70356-X R Core Team, “R: A language and environment for statistical computing” R Foundation for Statistical Computing, 2019. Disponible: https://www.r-project.org/
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spelling	López Martínez, César Andrésf21384acfaa1aaf5e3fa6bf31d962107300Hernández Riaño, Helman Enriquee9e7c99b3fe5423cb67cfc1290fc89d9500Soto de la Vega, Manuel Jesúsc4ce3d11043145084b6aa9b32d99cc013002019-07-01 00:00:002024-04-09T20:17:36Z2019-07-01 00:00:002024-04-09T20:17:36Z2019-07-010122-6517https://hdl.handle.net/11323/12248https://doi.org/10.17981/ingecuc.15.2.2019.1410.17981/ingecuc.15.2.2019.142382-4700Introducción: El problema Job Shop Con Operadores Calificados o Job Shop With Skilled Operators (JSSO) es una extensión del problema clásico Job Shop en el cual, una operación debe ser ejecutada por un conjunto limitados de trabajadores, con el objetivo de minimizar el tiempo de terminación total de los trabajos o Makespan, situación que puede representar distintas aplicaciones en la vida cotidiana. Es un problema complejo y es catalogado como NP-HARD. Objetivo: En este artículo, se aborda el problema JSSO desde la adaptación de un algoritmo conocido como Squirrel Search Algorithm (SSA). Metodología: Se propone un esquema de codificación discreto para el algoritmo SSA utilizando el método Smallest Position Value (SPV). Además, para evitar soluciones que violen las relaciones de precedencia; se corrigen con el método Valid Particle Generator (VPG), el cual garantiza soluciones factibles. Dos versiones del algoritmo se colocan a prueba en 28 instancias propuesta en la literatura para validar su rendimiento. Resultados: Los experimentos computacionales realizados muestran que los dos algoritmos propuestos alcanzan soluciones óptimas en 25 de las 28 instancias analizadas. Además, para las instancias en donde no se logró soluciones óptimas, el gap promedio no supera el 2% para ambas versiones de los algoritmos propuestos. Conclusiones: El esquema de codificación propuesto garantiza la discretización del algoritmo, generando soluciones que convergen hacia el óptimo. Además, la codificación propuesta, permite utilizar de manera natural los operadores de movimiento propuestos originalmente para el algoritmo utilizado. El rendimiento obtenido por los algoritmos es adecuado y de alta calidad.Introduction: The Job Shop problem With Skilled Operators (JSSO) is an extension of the classic Job Shop in which, an operation must be executed by a limited set of workers, aiming to minimize jobs total termination time or Makespan. This situation can represent different applications in daily life. JSSO is a complex problem and its classified as NP-HARD.. Objective: In this article, the JSSO problem is addressed. It is made by adapting an algorithm known as Squirrel Search Algorithm (SSA). Method: &nbsp;A discrete encoding scheme is proposed for the SSA algorithm and the Smallest Position Value (SPV) method are used. Also, solutions that can violate the precedent relationships are corrected with the Valid Particle Generator (VPG) method, which guarantees feasible solutions. Two versions of the algorithm were tested in 28 instances proposed in the literature to valid their performance. Results: Computer experiments show that the proposed algorithms reach optimal solutions in 25 and 28 analyzed instances. In addition, for the instances where optimality was not achieved, the average gap does not exceed the 2% for both versions of the proposed algorithms. Conclusions: The proposed encoding scheme guarantees the discretization of the algorithms, generating solutions that converge towards the optimum. In addition, the proposed encoding allows natural use of movement operators originally proposed for the algorithms used. Performance obtained by the algorithms is adequate and of high quality.application/pdftext/htmlapplication/xmlspaUniversidad de la CostaINGE CUC - 2019http://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/ingecuc/article/view/2572Combinatorial Optimizationswarm intelligencescheduling with operatorssmallest position valuevalid particle generatorcombinatorial optimizationinteligencia de enjambressecuenciación con operadoresposición del valor más pequeñogenerador válido de partículasptimización combinatoriaoptimización combinatoriaUn Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificadosA Discrete Squirrel Search Algorithm applied to the Job Shop problem with skilled operatorsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Inge Cuc A. Agnetis, G. Murgia and S. Sbrilli, “A job shop scheduling problem with human operators in handicraft production,” Int. J. Prod. Res., vol. 52, no. 13, pp. 3820–3831, Jul. 2014. https://doi.org/10.1080/00207543.2013.831220 M. Abdel-Basset, L. Abdel-Fatah and A. K. Sangaiah, “Metaheuristic Algorithms: A Comprehensive Review,” in Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications. Cambridge, MA, USA: Elsevier Inc., 2018, pp. 185–231. https://doi.org/10.1016/B978-0-12-813314-9.00010-4 M. Jain, V. Singh and A. Rani, “A novel nature-inspired algorithm for optimization: Squirrel search algorithm,” Swarm Evol. Comput., vol. 44, pp. 148–175, Feb. 2019. https://doi.org/10.1016/j.swevo.2018.02.013 L. Gao, X. Li, X. Wen, C. Lu and F. Wen, “A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem,” Comput. Ind. Eng., vol. 88, pp. 417–429, Oct. 2015. https://doi.org/10.1016/j.cie.2015.08.002 K. Z. Gao, P. N. Suganthan, T. J. Chua, C. S. Chong, T. X. Cai and Q. K. Pan, “A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion,” Expert Syst. Appl., vol. 42, no. 21, pp. 7652–7663, Nov. 2015. https://doi.org/10.1016/j.eswa.2015.06.004 K. Z. Gao, P. N. Suganthan, Q. K. Pan, T. J. Chua, C. S. Chong and T. X. Cai, “An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time,” Expert Syst. Appl., vol. 65, pp. 52–67, Dec. 2016. https://doi.org/10.1016/j.eswa.2016.07.046 A. Maroosi, R. C. Muniyandi, E. Sundararajan and A. M. Zin, “A parallel membrane inspired harmony search for optimization problems: A case study based on a flexible job shop scheduling problem,” Appl. Soft Comput. J., vol. 49, pp. 120–136, Dec. 2016. https://doi.org/10.1016/j.asoc.2016.08.007 A. Ahmadi-Javid and P. Hooshangi-Tabrizi, “Integrating employee timetabling with scheduling of machines and transporters in a job-shop environment: A mathematical formulation and an Anarchic Society Optimization algorithm,” Comput. Oper. Res., vol. 84, pp. 73–91, Aug. 2017. https://doi.org/10.1016/j.cor.2016.11.017 H. Piroozfard, K. Y. Wong and A. D. Asl, “An improved biogeography-based optimization for achieving optimal job shop scheduling solutions,” Procedia Comput. Sci., vol. 115, pp. 30–38, Aug. 2017. https://doi.org/10.1016/j.procs.2017.09.073 N. Sharma, H. Sharma and A. Sharma, “Beer froth artificial bee colony algorithm for job-shop scheduling problem,” Appl. Soft Comput. J., vol. 68, pp. 507–524, Jul. 2018. https://doi.org/10.1016/j.asoc.2018.04.001 B. Marzouki, O. B. Driss and K. Ghédira, “Solving Distributed and Flexible Job shop Scheduling Problem using a Chemical Reaction Optimization metaheuristic,” Procedia Comput. Sci., vol. 126, pp. 1424–1433, Jan. 2018. https://doi.org/10.1016/j.procs.2018.08.114 A. Agnetis, M. Flamini, G. Nicosia and A. Pacifici, “A job-shop problem with one additional resource type,” J. Sched., vol. 14, no. 3, pp. 225–237, Jun. 2011. https://doi.org/10.1007/s10951-010-0162-4 M. R. Sierra, C. Mencía and R. Varela, “New schedule generation schemes for the job-shop problem with operators,” J. Intell. Manuf., vol. 26, no. 3, pp. 511–525, Jul. 2013. https://doi.org/10.1007/s10845-013-0810-6 B. Giffler and G. L. Thompson, “Algorithms for Solving Production-Scheduling Problems,” Oper. Res., vol. 8, no. 4, pp. 487–503, Aug. 1960. https://doi.org/10.1287/opre.8.4.487 R. Mencia, M. R. Sierra, C. Mencia and R. Varela, “Genetic Algorithm for Job-Shop Scheduling with Operators,” in New Challenges on Bioinspired Applications, 4th International Work-conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spn, 30 May. - 3 Jun. 2011, pp. 305–314. https://doi.org/10.1007/978-3-642-21326-7 R. Mencía, M. R. Sierra, C. Mencía and R. Varela, “A genetic algorithm for job-shop scheduling with operators enhanced by weak Lamarckian evolution and search space narrowing,” Nat. Comput., vol. 13, no. 2, pp. 179–192, May. 2013. https://doi.org/10.1007/s11047-013-9373-x F. Barber, J. Escamilla, C. Mencia, M. Rodriguez-Molins, M. A. Salido and M. R. Sierra, “Robust solutions to job-shop scheduling problems with operators,” Proc. - Int. Conf. Tools with Artif. Intell. ICTAI, Athens, Greece, 7-9 Nov. 2012. https://doi.org/10.1109/ICTAI.2012.48 M. F. Tasgetiren, M. Sevkli, Y.-Ch. Liang and G. Gencyilmaz, “Particle swarm optimization algorithm for single machine total weighted tardiness problem,” Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), Portland, OR, USA, USA, 19-23 Jun. 2004, pp. 1412–1419. https://doi.org/10.1109/CEC.2004.1331062 R. Chaudhry, S. Tapaswi and N. Kumar, “Forwarding Zone enabled PSO routing with Network lifetime maximization in MANET,” Appl. Intell., vol. 48, no. 9, pp. 3053–3080, Sept. 2018. https://doi.org/10.1007/s10489-017-1127-5 I. Dubey and M. Gupta, “Uniform mutation and SPV rule based optimized PSO algorithm for TSP problem,” Proc. 2017 4th Int. Conf. Electron. Commun. Syst., ICECS 2017, Coimbatore, India, 24-25 Feb. 2017, pp. 168–172. https://doi.org/10.1109/ECS.2017.8067862N. Kumar and D. P. Vidyarthi, “A model for resource-constrained project scheduling using adaptive PSO,” Soft Comput., vol. 20, no. 4, pp. 1565–1580, Feb. 2015. https://doi.org/10.1007/s00500-015-1606-8R. L. Graham, E. L. Lawler, J. K. Lenstra and A. H. G. R. Kan, “Optimization and approximation in deterministic machine scheduling: a survey,” Ann. Discret. Math., vol. 5, pp. 287–326, 1979. https://doi.org/10.1016/S0167-5060(08)70356-XR Core Team, “R: A language and environment for statistical computing” R Foundation for Statistical Computing, 2019. Disponible: https://www.r-project.org/154143215https://revistascientificas.cuc.edu.co/ingecuc/article/download/2572/2505https://revistascientificas.cuc.edu.co/ingecuc/article/download/2572/2552https://revistascientificas.cuc.edu.co/ingecuc/article/download/2572/2671Núm. 2 , Año 2019 : (Julio-Diciembre)OREORE.xmltext/xml2695https://repositorio.cuc.edu.co/bitstream/11323/12248/1/ORE.xml0dd480572a4cb0a16cf1b9cc462af248MD51open access11323/12248oai:repositorio.cuc.edu.co:11323/122482024-04-09 15:17:36.954An error occurred on the license name.\|\|\|http://creativecommons.org/licenses/by-nc-nd/4.0metadata only accessRepositorio Universidad de La Costarepdigital@cuc.edu.co

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