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A simulated multi-objective model for flexible job shop transportation scheduling

Author

Listed:
  • Yiyi Xu

    (CESI-LINEACT laboratory
    Neoma Business School)

  • M’hammed Sahnoun

    (CESI-LINEACT laboratory)

  • Fouad Ben Abdelaziz

    (Neoma Business School)

  • David Baudry

    (CESI-LINEACT laboratory)

Abstract

This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The objectives considered in scheduling jobs and transportation tasks in a flexible job shop manufacturing system include makespan, robot travel distance, time difference with due date and critical waiting time. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:2:d:10.1007_s10479-020-03600-0
    DOI: 10.1007/s10479-020-03600-0
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    References listed on IDEAS

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    1. Maroua Nouiri & Abdelghani Bekrar & Abderezak Jemai & Smail Niar & Ahmed Chiheb Ammari, 2018. "An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 603-615, March.
    2. A. Charnes & W. W. Cooper & R. O. Ferguson, 1955. "Optimal Estimation of Executive Compensation by Linear Programming," Management Science, INFORMS, vol. 1(2), pages 138-151, January.
    3. 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.
    4. Peter J. M. van Laarhoven & Emile H. L. Aarts & Jan Karel Lenstra, 1992. "Job Shop Scheduling by Simulated Annealing," Operations Research, INFORMS, vol. 40(1), pages 113-125, February.
    5. Hurink, Johann & Knust, Sigrid, 2005. "Tabu search algorithms for job-shop problems with a single transport robot," European Journal of Operational Research, Elsevier, vol. 162(1), pages 99-111, April.
    6. Philip Doganis & Haralambos Sarimveis, 2008. "Optimal production scheduling for the dairy industry," Annals of Operations Research, Springer, vol. 159(1), pages 315-331, March.
    7. Stéphane Dauzère-Pérès & Jan Paulli, 1997. "An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search," Annals of Operations Research, Springer, vol. 70(0), pages 281-306, April.
    8. 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.
    9. K. Z. Gao & P. N. Suganthan & Q. K. Pan & T. J. Chua & T. X. Cai & C. S. Chong, 2016. "Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 363-374, April.
    10. De Giovanni, L. & Pezzella, F., 2010. "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 395-408, January.
    11. Rossi, Andrea, 2014. "Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships," International Journal of Production Economics, Elsevier, vol. 153(C), pages 253-267.
    12. Joseph Adams & Egon Balas & Daniel Zawack, 1988. "The Shifting Bottleneck Procedure for Job Shop Scheduling," Management Science, INFORMS, vol. 34(3), pages 391-401, March.
    13. Michael A. Langston, 1987. "Interstage Transportation Planning in the Deterministic Flow-Shop Environment," Operations Research, INFORMS, vol. 35(4), pages 556-564, August.
    14. Marchi, Ezio & Oviedo, Jorge A., 1992. "Lexicographic optimality in the multiple objective linear programming: The nucleolar solution," European Journal of Operational Research, Elsevier, vol. 57(3), pages 355-359, March.
    15. 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.
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