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Augmented ε-constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect

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  • Homa Amirian
  • Rashed Sahraeian

Abstract

This article addresses a general tri-objective non-permutation flowshop problem to minimise the makespan, the sum of flow time and maximum tardiness simultaneously. In order to enhance the applicability of the model, some practical assumptions are included. These are release dates, past sequence-dependent set-up times, a truncated generalisation of Dejong’s learning effect and predetermined machine availability constraints. First, the problem is formulated as a mixed-integer linear programming model. Second, the true Pareto front is achieved with augmented ε-constraint method for small-sized problems. Third, due to the high complexity of the model and the impractical computational times of larger instances, a heuristic algorithm based on the ε-constraint method is also proposed. Finally, the algorithms are tested to gauge their effectiveness, and the results are compared with other methods.

Suggested Citation

  • Homa Amirian & Rashed Sahraeian, 2015. "Augmented ε-constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect," International Journal of Production Research, Taylor & Francis Journals, vol. 53(19), pages 5962-5976, October.
  • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:19:p:5962-5976
    DOI: 10.1080/00207543.2015.1033033
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    Cited by:

    1. Derya Deliktaş, 2022. "Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 748-784, September.
    2. Bai, Danyu & Tang, Mengqian & Zhang, Zhi-Hai & Santibanez-Gonzalez, Ernesto DR, 2018. "Flow shop learning effect scheduling problem with release dates," Omega, Elsevier, vol. 78(C), pages 21-38.
    3. Choo Jun Tan & Siew Chin Neoh & Chee Peng Lim & Samer Hanoun & Wai Peng Wong & Chu Kong Loo & Li Zhang & Saeid Nahavandi, 2019. "Application of an evolutionary algorithm-based ensemble model to job-shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 879-890, February.
    4. Rossit, Daniel Alejandro & Tohmé, Fernando & Frutos, Mariano, 2018. "The Non-Permutation Flow-Shop scheduling problem: A literature review," Omega, Elsevier, vol. 77(C), pages 143-153.
    5. Shekarian, Mansoor & Reza Nooraie, Seyed Vahid & Parast, Mahour Mellat, 2020. "An examination of the impact of flexibility and agility on mitigating supply chain disruptions," International Journal of Production Economics, Elsevier, vol. 220(C).

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