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Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic

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  • Kacem, Imed
  • Hammadi, Slim
  • Borne, Pierre

Abstract

Most scheduling problems are complex combinatorial problems and very difficult to solve [Manage. Sci. 35 (1989) 164; F.S. Hillier, G.J. Lieberman, Introduction to Operations Research, Holden-Day, San Francisco, CA, 1967]. That is why, lots of methods focus on the optimization according to a single criterion (makespan, workloads of machines, waiting times, etc.). The combining of several criteria induces additional complexity and new problems. In this paper, we propose a Pareto approach based on the hybridization of fuzzy logic (FL) and evolutionary algorithms (EAs) to solve the flexible job-shop scheduling problem (FJSP). This hybrid approach exploits the knowledge representation capabilities of FL [Fuzzy Sets Syst. 1 (1989)] and the adaptive capabilities of EAs. The integration of these two methodologies for the multi-objective optimization has become an increasing interest. The objective considered is to minimize the overall completion time (makespan), the total workload of machines and the workload of the most loaded machine. Many examples are presented to illustrate some theoretical considerations and to show the efficiency of the suggested methodology.

Suggested Citation

  • Kacem, Imed & Hammadi, Slim & Borne, Pierre, 2002. "Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 245-276.
    • Handle: RePEc:eee:matcom:v:60:y:2002:i:3:p:245-276
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    References listed on IDEAS

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    1. J. Carlier & E. Pinson, 1989. "An Algorithm for Solving the Job-Shop Problem," Management Science, INFORMS, vol. 35(2), pages 164-176, February.
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    Cited by:

    1. Ho, Nhu Binh & Tay, Joc Cing & Lai, Edmund M.-K., 2007. "An effective architecture for learning and evolving flexible job-shop schedules," European Journal of Operational Research, Elsevier, vol. 179(2), pages 316-333, June.
    2. Zhang, Sicheng & Li, Xiang & Zhang, Bowen & Wang, Shouyang, 2020. "Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system," European Journal of Operational Research, Elsevier, vol. 283(2), pages 441-460.
    3. J. Behnamian, 2016. "Survey on fuzzy shop scheduling," Fuzzy Optimization and Decision Making, Springer, vol. 15(3), pages 331-366, September.
    4. Jacomine Grobler & Andries Engelbrecht & Schalk Kok & Sarma Yadavalli, 2010. "Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time," Annals of Operations Research, Springer, vol. 180(1), pages 165-196, November.
    5. Po-Hsiang Lu & Muh-Cherng Wu & Hao Tan & Yong-Han Peng & Chen-Fu Chen, 2018. "A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 19-34, January.
    6. Baykasoglu, Adil & ÖzbakIr, Lale, 2010. "Analyzing the effect of dispatching rules on the scheduling performance through grammar based flexible scheduling system," International Journal of Production Economics, Elsevier, vol. 124(2), pages 369-381, April.
    7. Wei Xiong & Dongmei Fu, 2018. "A new immune multi-agent system for the flexible job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 857-873, April.
    8. Li-Ning Xing & Ying-Wu Chen & Ke-Wei Yang, 2011. "Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems," Computational Optimization and Applications, Springer, vol. 48(1), pages 139-155, January.
    9. Loukil, Taicir & Teghem, Jacques & Fortemps, Philippe, 2007. "A multi-objective production scheduling case study solved by simulated annealing," European Journal of Operational Research, Elsevier, vol. 179(3), pages 709-722, June.
    10. Moslehi, Ghasem & Mahnam, Mehdi, 2011. "A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search," International Journal of Production Economics, Elsevier, vol. 129(1), pages 14-22, January.
    11. Saad, Ihsen & Hammadi, Slim & Benrejeb, Mohamed & Borne, Pierre, 2008. "Choquet integral for criteria aggregation in the flexible job-shop scheduling problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 76(5), pages 447-462.
    12. Bingke Yan & Bo Wang & Lin Zhu & Hesen Liu & Yilu Liu & Xingpei Ji & Dichen Liu, 2015. "A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet," Energies, MDPI, vol. 8(11), pages 1-24, November.
    13. Miguel A. Fernández Pérez & Fernanda M. P. Raupp, 2016. "A Newton-based heuristic algorithm for multi-objective flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 409-416, April.
    14. Raja Awais Liaqait & Shermeen Hamid & Salman Sagheer Warsi & Azfar Khalid, 2021. "A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    15. Chiang, Tsung-Che & Lin, Hsiao-Jou, 2013. "A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 87-98.
    16. Vilcot, Geoffrey & Billaut, Jean-Charles, 2008. "A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 190(2), pages 398-411, October.
    17. Abdelmaguid, Tamer F., 2015. "A neighborhood search function for flexible job shop scheduling with separable sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 260(C), pages 188-203.
    18. Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
    19. Julien Autuori & Faicel Hnaien & Farouk Yalaoui, 2016. "A mapping technique for better solution exploration: NSGA-II adaptation," Journal of Heuristics, Springer, vol. 22(1), pages 89-123, February.
    20. Yiyi Xu & M’hammed Sahnoun & Fouad Ben Abdelaziz & David Baudry, 2022. "A simulated multi-objective model for flexible job shop transportation scheduling," Annals of Operations Research, Springer, vol. 311(2), pages 899-920, April.
    21. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.
    22. Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.
    23. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.
    24. Miguel A. Ortíz & Leidy E. Betancourt & Kevin Parra Negrete & Fabio Felice & Antonella Petrillo, 2018. "Dispatching algorithm for production programming of flexible job-shop systems in the smart factory industry," Annals of Operations Research, Springer, vol. 264(1), pages 409-433, May.

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