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A hybrid multi-objective immune algorithm for predictive and reactive scheduling

Author

Listed:
  • Iwona Paprocka

    (Silesian University of Technology)

  • Bożena Skołud

    (Silesian University of Technology)

Abstract

The high productivity of a production process has a major impact on the reduction of the production cost and on a quick response to changing demands. Information about a failure-free machine operation time obtained in advance allows the users to plan preventive maintenance in order to keep the machine in a good operational condition. The introduction of maintenance work into a schedule reduces the frequency of unpredicted breaks caused by machine failures. It also results in higher productivity and in-time production. The foregoing of this constitutes the main idea of the predictive scheduling method proposed in the paper. Rescheduling of disrupted operations, with a minimal impact on the stability and robustness of a schedule, is the main idea of the reactive scheduling method proposed. The first objective of the paper is to present a hybrid multi-objective immune algorithm (H-MOIA) aided by heuristics: a minimal impact of disrupted operation on the schedule (MIDOS) for predictive scheduling and a minimal impact of rescheduled operation on the schedule (MIROS) for reactive scheduling. The second objective is to compare the H-MOIA with various methods for predictive and reactive scheduling. The H-MOIA + MIDOS is compared to two algorithms, identified in reference publications: (1) an algorithm based on priority rules: the least flexible job first (LFJ) and the longest processing time (LPT) (2) an Average Slack Method. The H-MOIA + MIROS is compared to: (1) an algorithm based on priority rules: the LFJ and LPT and (2) Shifted Gap-Reduction. This paper presents the research results and computer simulations.

Suggested Citation

  • Iwona Paprocka & Bożena Skołud, 2017. "A hybrid multi-objective immune algorithm for predictive and reactive scheduling," Journal of Scheduling, Springer, vol. 20(2), pages 165-182, April.
    • Handle: RePEc:spr:jsched:v:20:y:2017:i:2:d:10.1007_s10951-016-0494-9
    DOI: 10.1007/s10951-016-0494-9
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    References listed on IDEAS

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    1. Alejandra Duenas & Dobrila Petrovic, 2008. "An approach to predictive-reactive scheduling of parallel machines subject to disruptions," Annals of Operations Research, Springer, vol. 159(1), pages 65-82, March.
    2. Ayten Turkcan & M. Akturk & Robert Storer, 2009. "Predictive/reactive scheduling with controllable processing times and earliness-tardiness penalties," IISE Transactions, Taylor & Francis Journals, vol. 41(12), pages 1080-1095.
    3. Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.
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    Cited by:

    1. Iwona Paprocka, 2018. "Evaluation of the Effects of a Machine Failure on the Robustness of a Job Shop System—Proactive Approaches," Sustainability, MDPI, vol. 11(1), pages 1-18, December.
    2. Mohd Nor Akmal Khalid & Umi Kalsom Yusof, 2021. "Incorporating shifting bottleneck identification in assembly line balancing problem using an artificial immune system approach," Flexible Services and Manufacturing Journal, Springer, vol. 33(3), pages 717-749, September.

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