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A review and classification on distributed permutation flowshop scheduling problems

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  • Perez-Gonzalez, Paz
  • Framinan, Jose M.

Abstract

The Distributed Permutation Flowshop Scheduling (DPFS) problem is one of the fastest-growing topics in the scheduling literature, which in turn is among the most prolific fields in Operational Research (OR). Although the problem has been formally stated only twelve years ago, the number of papers on the topic is growing at a rapid pace, and the rising interest –both from academics and practitioners– on distributed manufacturing paradigms seems to indicate that this trend will continue to increase. Possibly as a side effect of this steady growth, the state-of-the-art on many decision problems within the field is far from being clear, with substantial overlaps in the solution procedures, lack of (fair) comparisons against existing methods, or the use of different denominations for the same problem, among other issues. In this paper, we carry out a review of the DPFS literature aimed at providing a classification and notation for DPFS problems under a common framework. Within this framework, contributions are exhaustively presented and discussed, together with the state-of-the-art of the problems and lines for future research.

Suggested Citation

  • Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
  • Handle: RePEc:eee:ejores:v:312:y:2024:i:1:p:1-21
    DOI: 10.1016/j.ejor.2023.02.001
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    References listed on IDEAS

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    1. Kai Wang & Yun Huang & Hu Qin, 2016. "A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(1), pages 68-82, January.
    2. Fernandez-Viagas, Victor & Ruiz, Rubén & Framinan, Jose M., 2017. "A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 707-721.
    3. Jun-qing Li & Shun-Chang Bai & Pei-yong Duan & Hong-yan Sang & Yu-yan Han & Zhi-xin Zheng, 2019. "An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 6922-6942, November.
    4. Ruiz, Ruben & Maroto, Concepcion, 2005. "A comprehensive review and evaluation of permutation flowshop heuristics," European Journal of Operational Research, Elsevier, vol. 165(2), pages 479-494, September.
    5. Guangchen Wang & Xinyu Li & Liang Gao & Peigen Li, 2022. "An effective multi-objective whale swarm algorithm for energy-efficient scheduling of distributed welding flow shop," Annals of Operations Research, Springer, vol. 310(1), pages 223-255, March.
    6. Vallada, Eva & Ruiz, Rubén & Framinan, Jose M., 2015. "New hard benchmark for flowshop scheduling problems minimising makespan," European Journal of Operational Research, Elsevier, vol. 240(3), pages 666-677.
    7. Wang, Sheng-yao & Wang, Ling & Liu, Min & Xu, Ye, 2013. "An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 145(1), pages 387-396.
    8. Chen-Yang Cheng & Kuo-Ching Ying & Hsia-Hsiang Chen & Hsiao-Shan Lu, 2019. "Minimising makespan in distributed mixed no-idle flowshops," International Journal of Production Research, Taylor & Francis Journals, vol. 57(1), pages 48-60, January.
    9. Taillard, E., 1993. "Benchmarks for basic scheduling problems," European Journal of Operational Research, Elsevier, vol. 64(2), pages 278-285, January.
    10. Naderi, Bahman & Ruiz, Rubén, 2014. "A scatter search algorithm for the distributed permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 239(2), pages 323-334.
    11. Ruiz, Ruben & Stutzle, Thomas, 2007. "A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2033-2049, March.
    12. Ding, Jian-Ya & Song, Shiji & Wu, Cheng, 2016. "Carbon-efficient scheduling of flow shops by multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 758-771.
    13. Fernandez-Viagas, Victor & Molina-Pariente, Jose M. & Framinan, Jose M., 2020. "Generalised accelerations for insertion-based heuristics in permutation flowshop scheduling," European Journal of Operational Research, Elsevier, vol. 282(3), pages 858-872.
    14. Arshad Ali & Yuvraj Gajpal & Tarek Y. Elmekkawy, 2021. "Distributed permutation flowshop scheduling problem with total completion time objective," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 425-447, June.
    15. Victor Fernandez-Viagas & Jose M. Framinan, 2015. "A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 53(4), pages 1111-1123, February.
    16. Ruiz, Rubén & Pan, Quan-Ke & Naderi, Bahman, 2019. "Iterated Greedy methods for the distributed permutation flowshop scheduling problem," Omega, Elsevier, vol. 83(C), pages 213-222.
    17. Pan, Quan-Ke & Ruiz, Rubén, 2014. "An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem," Omega, Elsevier, vol. 44(C), pages 41-50.
    18. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    19. Ankit Khare & Sunil Agrawal, 2021. "Effective heuristics and metaheuristics to minimise total tardiness for the distributed permutation flowshop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 59(23), pages 7266-7282, December.
    20. J M Framinan & J N D Gupta & R Leisten, 2004. "A review and classification of heuristics for permutation flow-shop scheduling with makespan objective," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1243-1255, December.
    21. Zhang, Xiandong & van de Velde, Steef, 2012. "Approximation algorithms for the parallel flow shop problem," European Journal of Operational Research, Elsevier, vol. 216(3), pages 544-552.
    22. Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
    23. Perez-Gonzalez, Paz & Framinan, Jose M., 2014. "A common framework and taxonomy for multicriteria scheduling problems with interfering and competing jobs: Multi-agent scheduling problems," European Journal of Operational Research, Elsevier, vol. 235(1), pages 1-16.
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