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A multi-point simulated annealing heuristic for solving multiple objective unrelated parallel machine scheduling problems

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  • Shih-Wei Lin
  • Kuo-Ching Ying

Abstract

This study considers the problem of job scheduling on unrelated parallel machines. A multi-objective multi-point simulated annealing (MOMSA) algorithm was proposed for solving this problem by simultaneously minimising makespan, total weighted completion time and total weighted tardiness. To assess the performance of the proposed heuristic and compare it with that of several benchmark heuristics, the obtained sets of non-dominated solutions were assessed using four multi-objective performance indicators. The computational results demonstrated that the proposed heuristic markedly outperformed the benchmark heuristics in terms of the four performance indicators. The proposed MOMSA algorithm can provide a new benchmark for future research related to the unrelated parallel machine scheduling problem addressed in this study.

Suggested Citation

  • Shih-Wei Lin & Kuo-Ching Ying, 2015. "A multi-point simulated annealing heuristic for solving multiple objective unrelated parallel machine scheduling problems," International Journal of Production Research, Taylor & Francis Journals, vol. 53(4), pages 1065-1076, February.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:4:p:1065-1076
    DOI: 10.1080/00207543.2014.942011
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    Cited by:

    1. Norelhouda Sekkal & Fayçal Belkaid, 2020. "A multi-objective simulated annealing to solve an identical parallel machine scheduling problem with deterioration effect and resources consumption constraints," Journal of Combinatorial Optimization, Springer, vol. 40(3), pages 660-696, October.
    2. Norelhouda Sekkal & Fayçal Belkaid, 0. "A multi-objective simulated annealing to solve an identical parallel machine scheduling problem with deterioration effect and resources consumption constraints," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-37.
    3. Rui Zhang, 2017. "Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search," Sustainability, MDPI, vol. 9(10), pages 1-26, September.
    4. Miao Tang & Minghua Hu & Honghai Zhang & Long Zhou, 2022. "Research on Multi Unmanned Aerial Vehicles Emergency Task Planning Method Based on Discrete Multi-Objective TLBO Algorithm," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    5. Wang, Haibo & Alidaee, Bahram, 2019. "Effective heuristic for large-scale unrelated parallel machines scheduling problems," Omega, Elsevier, vol. 83(C), pages 261-274.

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