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New hard benchmark for flowshop scheduling problems minimising makespan

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  • Vallada, Eva
  • Ruiz, Rubén
  • Framinan, Jose M.

Abstract

In this work a new benchmark of hard instances for the permutation flowshop scheduling problem with the objective of minimising the makespan is proposed. The new benchmark consists of 240 large instances and 240 small instances with up to 800 jobs and 60 machines. One of the objectives of the work is to generate a benchmark which satisfies the desired characteristics of any benchmark: comprehensive, amenable for statistical analysis and discriminant when several algorithms are compared. An exhaustive experimental procedure is carried out in order to select the hard instances, generating thousands of instances and selecting the hardest ones from the point of view of a gap computed as the difference between very good upper and lower bounds for each instance. Extensive generation and computational experiments, which have taken almost six years of combined CPU time, demonstrate that the proposed benchmark is harder and with more discriminant power than the most common benchmark from the literature. Moreover, a website is developed for researchers in order to share sets of instances, best known solutions and lower bounds, etc. for any combinatorial optimisation problem.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:240:y:2015:i:3:p:666-677
    DOI: 10.1016/j.ejor.2014.07.033
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    Cited by:

    1. Zewen Sun & Xingsheng Gu, 2017. "Hybrid Algorithm Based on an Estimation of Distribution Algorithm and Cuckoo Search for the No Idle Permutation Flow Shop Scheduling Problem with the Total Tardiness Criterion Minimization," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    2. Benavides, Alexander J. & Vera, Antony, 2022. "The reversibility property in a job-insertion tiebreaker for the permutational flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 297(2), pages 407-421.
    3. Framinan, Jose M. & Perez-Gonzalez, Paz, 2015. "On heuristic solutions for the stochastic flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 246(2), pages 413-420.
    4. Foumani, Mehdi & Smith-Miles, Kate, 2019. "The impact of various carbon reduction policies on green flowshop scheduling," Applied Energy, Elsevier, vol. 249(C), pages 300-315.
    5. Lobo, Fernando G. & Bazargani, Mosab & Burke, Edmund K., 2020. "A cutoff time strategy based on the coupon collector’s problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 101-114.
    6. Jan Gmys, 2022. "Exactly Solving Hard Permutation Flowshop Scheduling Problems on Peta-Scale GPU-Accelerated Supercomputers," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2502-2522, September.
    7. Pagnozzi, Federico & Stützle, Thomas, 2019. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems," European Journal of Operational Research, Elsevier, vol. 276(2), pages 409-421.
    8. Rossit, Daniel A. & Vásquez, Óscar C. & Tohmé, Fernando & Frutos, Mariano & Safe, Martín D., 2021. "A combinatorial analysis of the permutation and non-permutation flow shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 289(3), pages 841-854.
    9. Libralesso, Luc & Focke, Pablo Andres & Secardin, Aurélien & Jost, Vincent, 2022. "Iterative beam search algorithms for the permutation flowshop," European Journal of Operational Research, Elsevier, vol. 301(1), pages 217-234.
    10. Lin, Shih-Wei & Ying, Kuo-Ching, 2016. "Optimization of makespan for no-wait flowshop scheduling problems using efficient matheuristics," Omega, Elsevier, vol. 64(C), pages 115-125.
    11. Uchoa, Eduardo & Pecin, Diego & Pessoa, Artur & Poggi, Marcus & Vidal, Thibaut & Subramanian, Anand, 2017. "New benchmark instances for the Capacitated Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 257(3), pages 845-858.
    12. Fernandez-Viagas, Victor & Talens, Carla & Framinan, Jose M., 2022. "Assembly flowshop scheduling problem: Speed-up procedure and computational evaluation," European Journal of Operational Research, Elsevier, vol. 299(3), pages 869-882.
    13. Weibo Liu & Yan Jin & Mark Price, 2017. "New scheduling algorithms and digital tool for dynamic permutation flowshop with newly arrived order," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3234-3248, June.
    14. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Pasdeloup, Bastien & Meyer, Patrick, 2023. "Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1296-1330.
    15. Bahman Naderi & Rubén Ruiz & Vahid Roshanaei, 2023. "Mixed-Integer Programming vs. Constraint Programming for Shop Scheduling Problems: New Results and Outlook," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 817-843, July.
    16. Liu, Weibo & Jin, Yan & Price, Mark, 2017. "A new improved NEH heuristic for permutation flowshop scheduling problems," International Journal of Production Economics, Elsevier, vol. 193(C), pages 21-30.
    17. 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.
    18. Gmys, Jan & Mezmaz, Mohand & Melab, Nouredine & Tuyttens, Daniel, 2020. "A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 814-833.
    19. 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.
    20. Shuo Zhang & Jianyou Xu & Yingli Qiao, 2023. "Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems," Mathematics, MDPI, vol. 11(20), pages 1-25, October.

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    Keywords

    Flowshop; Makespan; Benchmark;
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