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A dispatching rule and a random iterated greedy metaheuristic for identical parallel machine scheduling to minimize total tardiness

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  • Cheng-Hsiung Lee

Abstract

This paper addresses a real-life production scheduling problem with identical parallel machines, originating from a plant producing Acrylonitrile-Butadiene-Styrene (ABS) plate products. In the considered practical scheduling problem, ABS plate has some specific specifications and each specification has several different levels. Because there is at least one different level of specification between two ABS plate products, it is necessary to make a set-up adjustment on each machine whenever a switch occurs from processing one ABS plate product to another product. As tardiness leads to extra penalty costs and opportunity losses, the objective of minimising total tardiness has become one of the most important tasks for the schedule manager in the plant. The problem can be classified as an identical parallel machine scheduling problem to minimise the total tardiness. A dispatching rule is proposed for this problem and evaluated by comparing it with the current scheduling method and several existing approaches. Moreover, an iterated greedy-based metaheuristic is developed to further improve the initial solution. The experimental results show that the proposed metaheuristic can perform better than an existing tabu search algorithm, and obtain the optimal solution for small-sized problems and significantly improve the initial solutions for large-sized problems.

Suggested Citation

  • Cheng-Hsiung Lee, 2018. "A dispatching rule and a random iterated greedy metaheuristic for identical parallel machine scheduling to minimize total tardiness," International Journal of Production Research, Taylor & Francis Journals, vol. 56(6), pages 2292-2308, March.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:6:p:2292-2308
    DOI: 10.1080/00207543.2017.1374571
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    Cited by:

    1. Julio Mar-Ortiz & Alex J. Ruiz Torres & Belarmino Adenso-Díaz, 2022. "Scheduling in parallel machines with two objectives: analysis of factors that influence the Pareto frontier," Operational Research, Springer, vol. 22(4), pages 4585-4605, September.
    2. Aykut Uzunoglu & Christian Gahm & Axel Tuma, 2024. "A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem," Annals of Operations Research, Springer, vol. 338(1), pages 407-428, July.
    3. Chung-Ho Su & Jen-Ya Wang, 2022. "A Branch-and-Bound Algorithm for Minimizing the Total Tardiness of Multiple Developers," Mathematics, MDPI, vol. 10(7), pages 1-24, April.

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