A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context

Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-crite...

Full description

Autores:
Ortíz-Barrios, Miguel
Petrillo, Antonella
De Felice, Fabio
Jaramillo-Rueda, Natalia
Jiménez-Delgado, Genett
Borrero-López, Luz
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8466
Acceso en línea:
https://hdl.handle.net/11323/8466
https://doi.org/10.3390/app11115107
https://repositorio.cuc.edu.co/
Palabra clave:
FJSP
MCDM
Fuzzy
AHP
TOPSIS
Smart manufacturing
Apparel industry
Optimization
Innovation
Decision analysis
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_d75a48d39825bfcbda3fb6ac0aead86f
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8466
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
title A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
spellingShingle A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
FJSP
MCDM
Fuzzy
AHP
TOPSIS
Smart manufacturing
Apparel industry
Optimization
Innovation
Decision analysis
title_short A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
title_full A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
title_fullStr A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
title_full_unstemmed A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
title_sort A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
dc.creator.fl_str_mv Ortíz-Barrios, Miguel
Petrillo, Antonella
De Felice, Fabio
Jaramillo-Rueda, Natalia
Jiménez-Delgado, Genett
Borrero-López, Luz
dc.contributor.author.spa.fl_str_mv Ortíz-Barrios, Miguel
Petrillo, Antonella
De Felice, Fabio
Jaramillo-Rueda, Natalia
Jiménez-Delgado, Genett
Borrero-López, Luz
dc.subject.spa.fl_str_mv FJSP
MCDM
Fuzzy
AHP
TOPSIS
Smart manufacturing
Apparel industry
Optimization
Innovation
Decision analysis
topic FJSP
MCDM
Fuzzy
AHP
TOPSIS
Smart manufacturing
Apparel industry
Optimization
Innovation
Decision analysis
description Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery. View Full-Text
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-07-14T13:04:49Z
dc.date.available.none.fl_str_mv 2021-07-14T13:04:49Z
dc.date.issued.none.fl_str_mv 2021-05-31
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 2076-3417
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8466
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.3390/app11115107
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2076-3417
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8466
https://doi.org/10.3390/app11115107
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Fu, Y.; Hou, Y.; Wang, Z.; Gao, K.; Wang, L. Distributed scheduling problems in intelligent manufacturing systems. Tsinghua Sci. Technol. 2021, 26, 625–645. [CrossRef]
2. Rahman, H.F.; Janardhanan, M.N.; Chuen, L.P.; Ponnambalam, S.G. Flowshop scheduling with sequence dependent setup times and batch delivery in supply chain. Comput. Ind. Eng. 2021, 158, 107378. [CrossRef]
3. Hakeem-Ur-Rehman; Wan, G.; Zhan, Y. Multi-level, multi-stage lot-sizing and scheduling in the flexible flow shop with demand information updating. Int. Trans. Oper. Res. 2021, 28, 2191–2217. [CrossRef]
4. Khalid, Q.S.; Azim, S.; Abas, M.; Babar, A.R.; Ahmad, I. Modified particle swarm algorithm for scheduling agricultural products. Eng. Sci. Technol. Int. J. 2021, 24, 818–828.
5. De Lacalle, L.N.L.; Lamikiz, A.; Salgado, M.A.; Herranz, S.; Rivero, A. Process planning for reliable high-speed machining of moulds. Int. J. Prod. Res. 2002, 40, 2789–2809. [CrossRef]
6. Che, Y.; Hu, K.; Zhang, Z.; Lim, A. Machine scheduling with orientation selection and two-dimensional packing for additive manufacturing. Comput. Oper. Res. 2021, 130, 105245. [CrossRef]
7. Lamikiz, A.; de Lacalle, L.N.L.; Sánchez, J.A.; Salgado, M.A. Cutting force integration at the CAM stage in the high-speed milling of complex surfaces. Int. J. Comput. Integr. Manuf. 2005, 18, 586–600. [CrossRef]
8. Wang, C.; Li, Y.; Li, X. Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm. J. Syst. Eng. Electron. 2021, 32, 261–271.
9. Sangaiah, A.K.; Suraki, M.Y.; Sadeghilalimi, M.; Hosseinabadi, A.A.R.; Wang, J. A new meta-heuristic algorithm for solving the flexible dynamic job-shop problem with parallel machines. Symmetry 2019, 11, 165. [CrossRef]
10. Vargas, J.; Calvo, R. Joint optimization of process flow and scheduling in service-oriented manufacturing systems. Materials 2018, 11, 1559. [CrossRef]
11. Fattahi, P.; Fallahi, A. Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability. CIRP J. Manuf. Sci. Technol. 2020, 2, 114–123. [CrossRef]
12. Wu, Z.; Weng, M.X. Multiagent scheduling method with earliness and tardiness objectives in flexible job shops. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2005, 35, 293–301. [CrossRef]
13. Cerve ˇnansk ˇ á, Z.; Važan, P.; Juhás, M.; Juhásová, B. Multi-criteria optimization in operations scheduling applying selected priority rules. Appl. Sci. 2021, 11, 2783. [CrossRef]
14. Sun, L.; Lin, L.; Gen, M.; Li, H. A hybrid cooperative coevolution algorithm for fuzzy flexible job shop scheduling. IEEE Trans. Fuzzy Syst. 2019, 27, 1008–1022. [CrossRef]
15. Gen, M.; Lin, L.; Zhang, W. Multiobjective hybrid genetic algorithms for manufacturing scheduling: Part I models and algorithms. Adv. Intell. Syst. Comput. 2015, 362, 3–25.
16. Li, Y.; Carabelli, S.; Fadda, E.; Tadei, R.; Terzo, O. Machine learning and optimization for production rescheduling in Industry 4.0. Int. J. Adv. Manuf. Technol. 2020, 110, 2445–2463. [CrossRef]
17. Ghaleb, M.; Zolfagharinia, H.; Taghipour, S. Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns. Comput. Oper. Res. 2020, 123, 105031. [CrossRef]
18. Mihoubi, B.; Bouzouia, B.; Gaham, M. Reactive scheduling approach for solving a realistic flexible job shop scheduling problem. Int. J. Prod. Res. 2020, 1–19. [CrossRef]
19. Lim, C.H.; Moon, S.K.; Okpoti, E.S. A Reusable Scheduling Problem Decomposition Framework for Smart Factories. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Macao, China, 15–18 December 2019; pp. 516–520.
20. Vazan, P.; Cervenanska, Z.; Kotianova, J.; Krizanova, G. The impact of selected priority rules on production goals. In Proceedings of the 2019 20th International Carpathian Control Conference, Krakow-Wieliczka, Poland, 26–29 May 2019.
21. Ma, A.; Nassehi, A.; Snider, C. Anarchic manufacturing. Int. J. Prod. Res. 2019, 57, 2514–2530. [CrossRef]
22. Dolgui, A.; Ivanov, D.; Sethi, S.P.; Sokolov, B. Scheduling in production, supply chain and Industry 4.0 systems by optimal control: Fundamentals, state-of-the-art and applications. Int. J. Prod. Res. 2019, 57, 411–432. [CrossRef]
23. Heger, J.; Voß, T. Dynamic priority based dispatching of AGVs in flexible job shops. Procedia CIRP 2019, 79, 445–449. [CrossRef]
24. Murín, S.; Rudová, H. Scheduling of Mobile Robots Using Constraint Programming. Lect. Notes Comput. Sci. 2019, 456–471.
25. Kim, J.W.; Kim, S.K. Interactive job sequencing system for small make-to-order manufacturers under smart manufacturing environment. Peer Peer Netw. Appl. 2019. [CrossRef]
26. Alves, F.; Varela, M.L.R.; Rocha, A.M.A.C.; Pereira, A.I.; Leitão, P. A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment. FME Trans. 2019, 47, 699–710. [CrossRef]
27. Gozali, L.; Kurniawan, V.; Nasution, S.R. Design of Job Scheduling System and Software for Packaging Process with SPT. EDD. LPT. CDS and NEH algorithm at PT. ACP. IOP Conf. Ser. Mater. Sci. Eng. 2019, 528, 012045. [CrossRef]
28. Lunardi, W.T.; Voos, H.; Cherri, L.H. An Imperialist Competitive Algorithm for a Real-World Flexible Job Shop Scheduling Problem. In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Turin, Italy, 4–7 September 2018; pp. 402–409.
29. Ortíz-Barrios, M.; Neira-Rodado, D.; Jiménez-Delgado, G.; Hernández-Palma, H. Using fahp-vikor for operation selection in the flexible job-shop scheduling problem: A case study in textile industry. Lect. Notes Comput. Sci. 2018. [CrossRef]
30. Wang, C.; Jiang, P.; Lu, T. The production instruction system for smart job shop. In Proceedings of the IEEE International Conference on Mechatronics and Automation, Harbin, China, 7–10 August 2016; pp. 1850–1854.
31. Bouazza, W.; Sallez, Y.; Beldjilali, B. A distributed approach solving partially flexible job-shop scheduling problem with a Q-learning effect. IFAC PapersOnLine 2017, 50, 15890–15895. [CrossRef]
32. Ivanov, D.; Dolgui, A.; Sokolov, B. A Dynamic Approach to Multi-stage Job Shop Scheduling in an Industry 4.0-Based Flexible Assembly System. IFIP Adv. Inf. Commun. Technol. 2017, 513, 475–482.
33. Son, D.S.; Lee, Y.H.; Shin, Y.W.; Moon, D.H. A simulation study on a flexible manufacturing systems for producing aircraft engine parts (WIP). Simul. Ser. 2017, 49, 205–210.
34. Yuan, G.C.; Bao, J.S.; Zhang, Q.W.; Li, Z.Q. An intelligent scheduling method for manufacturing system based on M2M. In Proceedings of the International Conference on Computers and Industrial Engineering, CIE, Lisbon, Portugal, 11–13 October 2017.
35. Jacob, E.; Astorga, J.; Unzilla, J.J.; García, D.; López-De-Lacalle, L.N. Towards a 5G compliant and flexible connected manufacturing facility | [Hacia una infraestructura de fabricación flexible, conectada e integrable en redes 5G]. Dyna 2018, 93, 656–662. [CrossRef]
36. Tian, S.; Wang, T.; Zhang, L.; Wu, X. The Internet of Things enabled manufacturing enterprise information system design and shop floor dynamic scheduling optimisation. Enterp. Inf. Syst. 2020, 14, 1238–1263. [CrossRef]
37. Zhao, F.; Hong, Y.; Yu, D.; Yang, Y.; Zhang, Q. A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems. Int. J. Comput. Integr. Manuf. 2010, 23, 20–39. [CrossRef]
38. Chen, Y.; Huang, C.; Chou, F.-D.; Huang, S. Single-machine scheduling problem with flexible maintenance and non-resumable jobs to minimise makespan. IET Collab. Intell. Manuf. 2020, 2, 174–181. [CrossRef]
39. Paprocka, I.; Kampa, A.; Gołda, G. The effects of a machine failure on the robustness of job shop systems-the predictive-reactive approach. Int. J. Mod. Manuf. Technol. 2019, 11, 72–79.
40. Kaya, ˙I.; Erdo ˘gan, M.; Kara¸san, A.; Özkan, B. Creating a road map for industry 4.0 by using an integrated fuzzy multicriteria decision-making methodology. Soft Comput. 2020, 24, 17931–17956. [CrossRef]
41. De Andrade, J.M.M.; De Leite, A.F.C.S.M.; Canciglieri, M.B.; De Loures, E.F.R.; Canciglieri, O. A multi-criteria approach for FMEA in product development in industry 4.0. Adv. Transdiscipl. Eng. 2020, 12, 311–320.
42. Yıldızba¸sı, A.; Ünlü, V. Performance evaluation of SMEs towards Industry 4.0 using fuzzy group decision making methods. SN Appl. Sci. 2020, 2, 355. [CrossRef]
43. Calleja, G.; Pastor, R. A dispatching algorithm for flexible job-shop scheduling with transfer batches: An industrial application. Prod. Plan. Control 2014, 25, 93–109. [CrossRef]
44. Utama, D.M. AHP and TOPSIS Integration for Green Supplier Selection: A Case Study in Indonesia. J. Phys. Conf. Ser. 2021, 1845, 012015. [CrossRef]
45. Ortiz-Barrios, M.; Gul, M.; López-Meza, P.; Yucesan, M.; Navarro-Jiménez, E. Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals. Int. J. Disaster Risk Reduct. 2020, 49, 101748. [CrossRef]
46. Wang, Y.; Xu, L.; Solangi, Y.A. Strategic renewable energy resources selection for Pakistan: Based on SWOT-Fuzzy AHP approach. Sustain. Cities Soc. 2020, 52, 101861. [CrossRef]
47. Pecchia, L.; Martin, J.L.; Ragozzino, A.; Vanzanella, C.; Scognamiglio, A.; Mirarchi, L.; Morgan, S.P. User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner. BMC Med. Inform. Decis. Mak. 2013, 13, 1–11. [CrossRef]
48. Liu, Y.; Eckert, C.M.; Earl, C. A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Syst. Appl. 2020, 161, 113738. [CrossRef]
49. Hwang, C.L.; Yoon, K.P. Multiple Attributes Decision Making Methods and Applications; Springer: Berlin, Germany, 1981.
50. Krohling, R.A.; Pacheco, A.G.C. A-TOPSIS—An approach based on TOPSIS for ranking evolutionary algorithms. Pap. Presented Procedia Comput. Sci. 2015, 55, 308–317. [CrossRef]
51. Ezhilarasan, N.; Vijayalakshmi, C. Optimization of fuzzy programming with TOPSIS algorithm. Pap. Presented Procedia Comput. Sci. 2020, 172, 473–479. [CrossRef]
52. Ortíz, M.A.; Betancourt, L.E.; Negrete, K.P.; De Felice, F.; Petrillo, A. Dispatching algorithm for production programming of flexible job-shop systems in the smart factory industry. Ann. Oper. Res. 2018, 264, 409–433. [CrossRef]
53. Liu, H.; Wang, L.; Li, Z.; Hu, Y. Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. IEEE Trans. Fuzzy Syst. 2019, 27, 84–95. [CrossRef]
54. Jiménez-Delgado, G.; Santos, G.; Félix, M.J.; Teixeira, P.; Sá, J.C. A combined ahp-topsis approach for evaluating the process of innovation and integration of management systems in the logistic sector. In HCI International 2020–Late Breaking Papers: Interaction, Knowledge and Social Media; Springer: Cham, Switzerland, 2020. [CrossRef]
55. Bid, S.; Siddique, G. Human risk assessment of panchet dam in India using TOPSIS and WASPAS multi-criteria decision-making (MCDM) methods. Heliyon 2019, 5. [CrossRef] [PubMed]
56. Zhang, F.; Mei, Y.; Nguyen, S.; Zhang, M. Collaborative multifidelity-based surrogate models for genetic programming in dynamic flexible job shop scheduling. IEEE Trans. Cybern. 2021. [CrossRef]
57. Ortíz, M. Theory of constraints and LP modeling as strategic decision tools for productivity increasing in the towel line of a textile-confection sector company. Prospectiva 2013. [CrossRef]
58. Ortiz, M.; Neira, D.; Jiménez, G.; Hernández, H. Solving flexible job-shop scheduling problem with transfer batches, setup times and multiple resources in apparel industry. In Proceedings of the International Conference on Swarm Intelligence, Belgrade, Serbia, 14–20 July 2016. [CrossRef]
dc.rights.spa.fl_str_mv CC0 1.0 Universal
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/publicdomain/zero/1.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv Applied Sciences
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://www.mdpi.com/2076-3417/11/11/5107
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/250308d7-3fdb-46ce-aa12-f6acf0141a9a/download
https://repositorio.cuc.edu.co/bitstreams/e581c0bc-3afc-4188-877b-c353466a78f5/download
https://repositorio.cuc.edu.co/bitstreams/98f38b81-7874-4d9e-9de2-e12b19ac240b/download
https://repositorio.cuc.edu.co/bitstreams/2c53b30d-b046-495e-8364-8ad14669f52e/download
https://repositorio.cuc.edu.co/bitstreams/d5ec2319-159a-465d-aa2b-7d183924fcea/download
bitstream.checksum.fl_str_mv 4d1c56e76a945b59f14cdd582902e836
42fd4ad1e89814f5e4a476b409eb708c
e30e9215131d99561d40d6b0abbe9bad
d5e72a9f18ba5494583ab434bfafaca1
67b0f3ac249142601e1e00e5271e9e24
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1828166859834261504
spelling Ortíz-Barrios, MiguelPetrillo, AntonellaDe Felice, FabioJaramillo-Rueda, NataliaJiménez-Delgado, GenettBorrero-López, Luz2021-07-14T13:04:49Z2021-07-14T13:04:49Z2021-05-312076-3417https://hdl.handle.net/11323/8466https://doi.org/10.3390/app11115107Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery. View Full-TextOrtíz-Barrios, MiguelPetrillo, Antonella-will be generated-orcid-0000-0003-2465-5370-600De Felice, Fabio-will be generated-orcid-0000-0002-2138-2103-600Jaramillo-Rueda, NataliaJiménez-Delgado, GenettBorrero-López, Luzapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Applied Scienceshttps://www.mdpi.com/2076-3417/11/11/5107FJSPMCDMFuzzyAHPTOPSISSmart manufacturingApparel industryOptimizationInnovationDecision analysisA dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 contextArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Fu, Y.; Hou, Y.; Wang, Z.; Gao, K.; Wang, L. Distributed scheduling problems in intelligent manufacturing systems. Tsinghua Sci. Technol. 2021, 26, 625–645. [CrossRef]2. Rahman, H.F.; Janardhanan, M.N.; Chuen, L.P.; Ponnambalam, S.G. Flowshop scheduling with sequence dependent setup times and batch delivery in supply chain. Comput. Ind. Eng. 2021, 158, 107378. [CrossRef]3. Hakeem-Ur-Rehman; Wan, G.; Zhan, Y. Multi-level, multi-stage lot-sizing and scheduling in the flexible flow shop with demand information updating. Int. Trans. Oper. Res. 2021, 28, 2191–2217. [CrossRef]4. Khalid, Q.S.; Azim, S.; Abas, M.; Babar, A.R.; Ahmad, I. Modified particle swarm algorithm for scheduling agricultural products. Eng. Sci. Technol. Int. J. 2021, 24, 818–828.5. De Lacalle, L.N.L.; Lamikiz, A.; Salgado, M.A.; Herranz, S.; Rivero, A. Process planning for reliable high-speed machining of moulds. Int. J. Prod. Res. 2002, 40, 2789–2809. [CrossRef]6. Che, Y.; Hu, K.; Zhang, Z.; Lim, A. Machine scheduling with orientation selection and two-dimensional packing for additive manufacturing. Comput. Oper. Res. 2021, 130, 105245. [CrossRef]7. Lamikiz, A.; de Lacalle, L.N.L.; Sánchez, J.A.; Salgado, M.A. Cutting force integration at the CAM stage in the high-speed milling of complex surfaces. Int. J. Comput. Integr. Manuf. 2005, 18, 586–600. [CrossRef]8. Wang, C.; Li, Y.; Li, X. Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm. J. Syst. Eng. Electron. 2021, 32, 261–271.9. Sangaiah, A.K.; Suraki, M.Y.; Sadeghilalimi, M.; Hosseinabadi, A.A.R.; Wang, J. A new meta-heuristic algorithm for solving the flexible dynamic job-shop problem with parallel machines. Symmetry 2019, 11, 165. [CrossRef]10. Vargas, J.; Calvo, R. Joint optimization of process flow and scheduling in service-oriented manufacturing systems. Materials 2018, 11, 1559. [CrossRef]11. Fattahi, P.; Fallahi, A. Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability. CIRP J. Manuf. Sci. Technol. 2020, 2, 114–123. [CrossRef]12. Wu, Z.; Weng, M.X. Multiagent scheduling method with earliness and tardiness objectives in flexible job shops. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2005, 35, 293–301. [CrossRef]13. Cerve ˇnansk ˇ á, Z.; Važan, P.; Juhás, M.; Juhásová, B. Multi-criteria optimization in operations scheduling applying selected priority rules. Appl. Sci. 2021, 11, 2783. [CrossRef]14. Sun, L.; Lin, L.; Gen, M.; Li, H. A hybrid cooperative coevolution algorithm for fuzzy flexible job shop scheduling. IEEE Trans. Fuzzy Syst. 2019, 27, 1008–1022. [CrossRef]15. Gen, M.; Lin, L.; Zhang, W. Multiobjective hybrid genetic algorithms for manufacturing scheduling: Part I models and algorithms. Adv. Intell. Syst. Comput. 2015, 362, 3–25.16. Li, Y.; Carabelli, S.; Fadda, E.; Tadei, R.; Terzo, O. Machine learning and optimization for production rescheduling in Industry 4.0. Int. J. Adv. Manuf. Technol. 2020, 110, 2445–2463. [CrossRef]17. Ghaleb, M.; Zolfagharinia, H.; Taghipour, S. Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns. Comput. Oper. Res. 2020, 123, 105031. [CrossRef]18. Mihoubi, B.; Bouzouia, B.; Gaham, M. Reactive scheduling approach for solving a realistic flexible job shop scheduling problem. Int. J. Prod. Res. 2020, 1–19. [CrossRef]19. Lim, C.H.; Moon, S.K.; Okpoti, E.S. A Reusable Scheduling Problem Decomposition Framework for Smart Factories. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Macao, China, 15–18 December 2019; pp. 516–520.20. Vazan, P.; Cervenanska, Z.; Kotianova, J.; Krizanova, G. The impact of selected priority rules on production goals. In Proceedings of the 2019 20th International Carpathian Control Conference, Krakow-Wieliczka, Poland, 26–29 May 2019.21. Ma, A.; Nassehi, A.; Snider, C. Anarchic manufacturing. Int. J. Prod. Res. 2019, 57, 2514–2530. [CrossRef]22. Dolgui, A.; Ivanov, D.; Sethi, S.P.; Sokolov, B. Scheduling in production, supply chain and Industry 4.0 systems by optimal control: Fundamentals, state-of-the-art and applications. Int. J. Prod. Res. 2019, 57, 411–432. [CrossRef]23. Heger, J.; Voß, T. Dynamic priority based dispatching of AGVs in flexible job shops. Procedia CIRP 2019, 79, 445–449. [CrossRef]24. Murín, S.; Rudová, H. Scheduling of Mobile Robots Using Constraint Programming. Lect. Notes Comput. Sci. 2019, 456–471.25. Kim, J.W.; Kim, S.K. Interactive job sequencing system for small make-to-order manufacturers under smart manufacturing environment. Peer Peer Netw. Appl. 2019. [CrossRef]26. Alves, F.; Varela, M.L.R.; Rocha, A.M.A.C.; Pereira, A.I.; Leitão, P. A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment. FME Trans. 2019, 47, 699–710. [CrossRef]27. Gozali, L.; Kurniawan, V.; Nasution, S.R. Design of Job Scheduling System and Software for Packaging Process with SPT. EDD. LPT. CDS and NEH algorithm at PT. ACP. IOP Conf. Ser. Mater. Sci. Eng. 2019, 528, 012045. [CrossRef]28. Lunardi, W.T.; Voos, H.; Cherri, L.H. An Imperialist Competitive Algorithm for a Real-World Flexible Job Shop Scheduling Problem. In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Turin, Italy, 4–7 September 2018; pp. 402–409.29. Ortíz-Barrios, M.; Neira-Rodado, D.; Jiménez-Delgado, G.; Hernández-Palma, H. Using fahp-vikor for operation selection in the flexible job-shop scheduling problem: A case study in textile industry. Lect. Notes Comput. Sci. 2018. [CrossRef]30. Wang, C.; Jiang, P.; Lu, T. The production instruction system for smart job shop. In Proceedings of the IEEE International Conference on Mechatronics and Automation, Harbin, China, 7–10 August 2016; pp. 1850–1854.31. Bouazza, W.; Sallez, Y.; Beldjilali, B. A distributed approach solving partially flexible job-shop scheduling problem with a Q-learning effect. IFAC PapersOnLine 2017, 50, 15890–15895. [CrossRef]32. Ivanov, D.; Dolgui, A.; Sokolov, B. A Dynamic Approach to Multi-stage Job Shop Scheduling in an Industry 4.0-Based Flexible Assembly System. IFIP Adv. Inf. Commun. Technol. 2017, 513, 475–482.33. Son, D.S.; Lee, Y.H.; Shin, Y.W.; Moon, D.H. A simulation study on a flexible manufacturing systems for producing aircraft engine parts (WIP). Simul. Ser. 2017, 49, 205–210.34. Yuan, G.C.; Bao, J.S.; Zhang, Q.W.; Li, Z.Q. An intelligent scheduling method for manufacturing system based on M2M. In Proceedings of the International Conference on Computers and Industrial Engineering, CIE, Lisbon, Portugal, 11–13 October 2017.35. Jacob, E.; Astorga, J.; Unzilla, J.J.; García, D.; López-De-Lacalle, L.N. Towards a 5G compliant and flexible connected manufacturing facility | [Hacia una infraestructura de fabricación flexible, conectada e integrable en redes 5G]. Dyna 2018, 93, 656–662. [CrossRef]36. Tian, S.; Wang, T.; Zhang, L.; Wu, X. The Internet of Things enabled manufacturing enterprise information system design and shop floor dynamic scheduling optimisation. Enterp. Inf. Syst. 2020, 14, 1238–1263. [CrossRef]37. Zhao, F.; Hong, Y.; Yu, D.; Yang, Y.; Zhang, Q. A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems. Int. J. Comput. Integr. Manuf. 2010, 23, 20–39. [CrossRef]38. Chen, Y.; Huang, C.; Chou, F.-D.; Huang, S. Single-machine scheduling problem with flexible maintenance and non-resumable jobs to minimise makespan. IET Collab. Intell. Manuf. 2020, 2, 174–181. [CrossRef]39. Paprocka, I.; Kampa, A.; Gołda, G. The effects of a machine failure on the robustness of job shop systems-the predictive-reactive approach. Int. J. Mod. Manuf. Technol. 2019, 11, 72–79.40. Kaya, ˙I.; Erdo ˘gan, M.; Kara¸san, A.; Özkan, B. Creating a road map for industry 4.0 by using an integrated fuzzy multicriteria decision-making methodology. Soft Comput. 2020, 24, 17931–17956. [CrossRef]41. De Andrade, J.M.M.; De Leite, A.F.C.S.M.; Canciglieri, M.B.; De Loures, E.F.R.; Canciglieri, O. A multi-criteria approach for FMEA in product development in industry 4.0. Adv. Transdiscipl. Eng. 2020, 12, 311–320.42. Yıldızba¸sı, A.; Ünlü, V. Performance evaluation of SMEs towards Industry 4.0 using fuzzy group decision making methods. SN Appl. Sci. 2020, 2, 355. [CrossRef]43. Calleja, G.; Pastor, R. A dispatching algorithm for flexible job-shop scheduling with transfer batches: An industrial application. Prod. Plan. Control 2014, 25, 93–109. [CrossRef]44. Utama, D.M. AHP and TOPSIS Integration for Green Supplier Selection: A Case Study in Indonesia. J. Phys. Conf. Ser. 2021, 1845, 012015. [CrossRef]45. Ortiz-Barrios, M.; Gul, M.; López-Meza, P.; Yucesan, M.; Navarro-Jiménez, E. Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals. Int. J. Disaster Risk Reduct. 2020, 49, 101748. [CrossRef]46. Wang, Y.; Xu, L.; Solangi, Y.A. Strategic renewable energy resources selection for Pakistan: Based on SWOT-Fuzzy AHP approach. Sustain. Cities Soc. 2020, 52, 101861. [CrossRef]47. Pecchia, L.; Martin, J.L.; Ragozzino, A.; Vanzanella, C.; Scognamiglio, A.; Mirarchi, L.; Morgan, S.P. User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner. BMC Med. Inform. Decis. Mak. 2013, 13, 1–11. [CrossRef]48. Liu, Y.; Eckert, C.M.; Earl, C. A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Syst. Appl. 2020, 161, 113738. [CrossRef]49. Hwang, C.L.; Yoon, K.P. Multiple Attributes Decision Making Methods and Applications; Springer: Berlin, Germany, 1981.50. Krohling, R.A.; Pacheco, A.G.C. A-TOPSIS—An approach based on TOPSIS for ranking evolutionary algorithms. Pap. Presented Procedia Comput. Sci. 2015, 55, 308–317. [CrossRef]51. Ezhilarasan, N.; Vijayalakshmi, C. Optimization of fuzzy programming with TOPSIS algorithm. Pap. Presented Procedia Comput. Sci. 2020, 172, 473–479. [CrossRef]52. Ortíz, M.A.; Betancourt, L.E.; Negrete, K.P.; De Felice, F.; Petrillo, A. Dispatching algorithm for production programming of flexible job-shop systems in the smart factory industry. Ann. Oper. Res. 2018, 264, 409–433. [CrossRef]53. Liu, H.; Wang, L.; Li, Z.; Hu, Y. Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. IEEE Trans. Fuzzy Syst. 2019, 27, 84–95. [CrossRef]54. Jiménez-Delgado, G.; Santos, G.; Félix, M.J.; Teixeira, P.; Sá, J.C. A combined ahp-topsis approach for evaluating the process of innovation and integration of management systems in the logistic sector. In HCI International 2020–Late Breaking Papers: Interaction, Knowledge and Social Media; Springer: Cham, Switzerland, 2020. [CrossRef]55. Bid, S.; Siddique, G. Human risk assessment of panchet dam in India using TOPSIS and WASPAS multi-criteria decision-making (MCDM) methods. Heliyon 2019, 5. [CrossRef] [PubMed]56. Zhang, F.; Mei, Y.; Nguyen, S.; Zhang, M. Collaborative multifidelity-based surrogate models for genetic programming in dynamic flexible job shop scheduling. IEEE Trans. Cybern. 2021. [CrossRef]57. Ortíz, M. Theory of constraints and LP modeling as strategic decision tools for productivity increasing in the towel line of a textile-confection sector company. Prospectiva 2013. [CrossRef]58. Ortiz, M.; Neira, D.; Jiménez, G.; Hernández, H. Solving flexible job-shop scheduling problem with transfer batches, setup times and multiple resources in apparel industry. In Proceedings of the International Conference on Swarm Intelligence, Belgrade, Serbia, 14–20 July 2016. [CrossRef]PublicationORIGINALA Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling.pdfA Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling.pdfapplication/pdf3136043https://repositorio.cuc.edu.co/bitstreams/250308d7-3fdb-46ce-aa12-f6acf0141a9a/download4d1c56e76a945b59f14cdd582902e836MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/e581c0bc-3afc-4188-877b-c353466a78f5/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/98f38b81-7874-4d9e-9de2-e12b19ac240b/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILA Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling.pdf.jpgA Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling.pdf.jpgimage/jpeg74171https://repositorio.cuc.edu.co/bitstreams/2c53b30d-b046-495e-8364-8ad14669f52e/downloadd5e72a9f18ba5494583ab434bfafaca1MD54TEXTA Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling.pdf.txtA Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling.pdf.txttext/plain74710https://repositorio.cuc.edu.co/bitstreams/d5ec2319-159a-465d-aa2b-7d183924fcea/download67b0f3ac249142601e1e00e5271e9e24MD5511323/8466oai:repositorio.cuc.edu.co:11323/84662024-09-17 14:19:07.103http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares