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Permutation flowshop scheduling to minimize the total tardiness with learning effects

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  • Lee, Wen-Chiung
  • Chung, Yu-Hsiang

Abstract

Scheduling with learning effects has received considerable attention recently. Often, numbers of operations have to be done on every job in many manufacturing and assembly facilities. However, it is seldom discussed in the general multiple-machine setting, especially without the assumptions of identical processing time on all the machines or dominant machines. With the current emphasis of customer service and meeting the promised delivery dates, we consider a permutation flowshop scheduling problem with learning effects where the objective is to minimize the total tardiness. A branch-and-bound algorithm and two heuristic algorithms are established to search for the optimal and near-optimal solutions. Computational experiments are also given to evaluate the performance of the algorithms.

Suggested Citation

  • Lee, Wen-Chiung & Chung, Yu-Hsiang, 2013. "Permutation flowshop scheduling to minimize the total tardiness with learning effects," International Journal of Production Economics, Elsevier, vol. 141(1), pages 327-334.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:327-334
    DOI: 10.1016/j.ijpe.2012.08.014
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    References listed on IDEAS

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    Cited by:

    1. Tzu-Li Chen & Chen-Yang Cheng & Yi-Han Chou, 2020. "Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming," Annals of Operations Research, Springer, vol. 290(1), pages 813-836, July.
    2. Lin-Hui Sun & Kai Cui & Ju-Hong Chen & Jun Wang & Xian-Chen He, 2013. "Some results of the worst-case analysis for flow shop scheduling with a learning effect," Annals of Operations Research, Springer, vol. 211(1), pages 481-490, December.

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