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Flow shop scheduling with general position weighted learning effects to minimise total weighted completion time

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  • Xinyu Sun
  • Xin-Na Geng
  • Feng Liu

Abstract

This article considers the flow shop problem of minimising the total weighted completion time in which the processing times of jobs are variable according to general position weighted learning effects. Two simple heuristics are proposed, and their worst-case error bounds are analysed. In addition, some complex heuristics (including simulated annealing algorithms) and a branch-and-bound algorithm are proposed as solutions to this problem. Finally, computational experiments are performed to examine the effectiveness and efficiency of the proposed algorithms.

Suggested Citation

  • Xinyu Sun & Xin-Na Geng & Feng Liu, 2021. "Flow shop scheduling with general position weighted learning effects to minimise total weighted completion time," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(12), pages 2674-2689, December.
    • Handle: RePEc:taf:tjorxx:v:72:y:2021:i:12:p:2674-2689
    DOI: 10.1080/01605682.2020.1806746
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    Cited by:

    1. Yi-Chun Wang & Ji-Bo Wang, 2023. "Study on Convex Resource Allocation Scheduling with a Time-Dependent Learning Effect," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    2. Zong-Jun Wei & Li-Yan Wang & Lei Zhang & Ji-Bo Wang & Ershen Wang, 2023. "Single-Machine Maintenance Activity Scheduling with Convex Resource Constraints and Learning Effects," Mathematics, MDPI, vol. 11(16), pages 1-21, August.

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