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Solving the flexible job shop scheduling problem with sequence-dependent setup times

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  • Shen, Liji
  • Dauzère-Pérès, Stéphane
  • Neufeld, Janis S.

Abstract

This paper addresses the flexible job shop scheduling problem with sequence-dependent setup times and where the objective is to minimize the makespan. We first present a mathematical model which can solve small instances to optimality, and also serves as a problem representation. After studying structural properties of the problem using a disjunctive graph model, we develop a tabu search algorithm with specific neighborhood functions and a diversification structure. Extensive experiments are conducted on benchmark instances. Test results first show that our algorithm outperforms most existing approaches for the classical flexible job shop scheduling problem. Additional experiments also confirm the significant improvement achieved by integrating the propositions introduced in this study.

Suggested Citation

  • Shen, Liji & Dauzère-Pérès, Stéphane & Neufeld, Janis S., 2018. "Solving the flexible job shop scheduling problem with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 265(2), pages 503-516.
  • Handle: RePEc:eee:ejores:v:265:y:2018:i:2:p:503-516
    DOI: 10.1016/j.ejor.2017.08.021
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    References listed on IDEAS

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    Cited by:

    1. Tamssaouet, Karim & Dauzère-Pérès, Stéphane, 2023. "A general efficient neighborhood structure framework for the job-shop and flexible job-shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 311(2), pages 455-471.
    2. Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2021. "Production and transport scheduling in flexible job shop manufacturing systems," Journal of Global Optimization, Springer, vol. 79(2), pages 463-502, February.
    3. Haicao Song & Pan Liu, 2022. "A Study on the Optimal Flexible Job-Shop Scheduling with Sequence-Dependent Setup Time Based on a Hybrid Algorithm of Improved Quantum Cat Swarm Optimization," Sustainability, MDPI, vol. 14(15), pages 1-16, August.
    4. Chia-Yen Lee & Chen-Fu Chien, 2022. "Pitfalls and protocols of data science in manufacturing practice," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1189-1207, June.
    5. uit het Broek, Michiel A.J. & Van der Heide, Gerlach & Van Foreest, Nicky D., 2020. "Energy-saving policies for temperature-controlled production systems with state-dependent setup times and costs," European Journal of Operational Research, Elsevier, vol. 287(3), pages 916-928.
    6. Berterottière, Lucas & Dauzère-Pérès, Stéphane & Yugma, Claude, 2024. "Flexible job-shop scheduling with transportation resources," European Journal of Operational Research, Elsevier, vol. 312(3), pages 890-909.
    7. Miguel S. E. Martins & Joaquim L. Viegas & Tiago Coito & Bernardo Firme & Andrea Costigliola & João Figueiredo & Susana M. Vieira & João M. C. Sousa, 2023. "Minimizing total completion time in large-sized pharmaceutical quality control scheduling," Journal of Heuristics, Springer, vol. 29(1), pages 177-206, February.
    8. Gregory A. Kasapidis & Dimitris C. Paraskevopoulos & Panagiotis P. Repoussis & Christos D. Tarantilis, 2021. "Flexible Job Shop Scheduling Problems with Arbitrary Precedence Graphs," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4044-4068, November.
    9. Fátima Pilar & Eliana Costa e Silva & Ana Borges, 2023. "Optimizing Vehicle Repairs Scheduling Using Mixed Integer Linear Programming: A Case Study in the Portuguese Automobile Sector," Mathematics, MDPI, vol. 11(11), pages 1-23, June.
    10. Liang Tang & Zhihong Jin & Xuwei Qin & Ke Jing, 2019. "Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design," Annals of Operations Research, Springer, vol. 275(2), pages 685-714, April.
    11. Leilei Meng & Biao Zhang & Kaizhou Gao & Peng Duan, 2022. "An MILP Model for Energy-Conscious Flexible Job Shop Problem with Transportation and Sequence-Dependent Setup Times," Sustainability, MDPI, vol. 15(1), pages 1-14, December.
    12. Wieslaw Kubiak & Yanling Feng & Guo Li & Suresh P. Sethi & Chelliah Sriskandarajah, 2020. "Efficient algorithms for flexible job shop scheduling with parallel machines," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(4), pages 272-288, June.
    13. Shen, Liji & Dauzère-Pérès, Stéphane & Maecker, Söhnke, 2023. "Energy cost efficient scheduling in flexible job-shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 310(3), pages 992-1016.
    14. Fei Luan & Zongyan Cai & Shuqiang Wu & Shi Qiang Liu & Yixin He, 2019. "Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    15. Neufeld, Janis S. & Schulz, Sven & Buscher, Udo, 2023. "A systematic review of multi-objective hybrid flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 309(1), pages 1-23.
    16. M. Hajibabaei & J. Behnamian, 2023. "Fuzzy cleaner production in assembly flexible job-shop scheduling with machine breakdown and batch transportation: Lagrangian relaxation," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-26, July.
    17. Shen, Liji & Dauzère-Pérès, Stéphane & Neufeld, Janis S., 2022. "Corrigendum to: “Solving the flexible job shop scheduling problem with sequence-dependent setup times”, [European Journal of Operational Research 265, 503–516, 2018]," European Journal of Operational Research, Elsevier, vol. 296(2), pages 748-748.
    18. Jin Xu & Natarajan Gautam, 2020. "On competitive analysis for polling systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(6), pages 404-419, September.

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