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Rescheduling of unrelated parallel machines with job-dependent setup times under forecasted machine breakdown

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  • Young-In Kim
  • Hyun-Jung Kim

Abstract

We address a rescheduling problem of unrelated parallel machines with job-dependent setup times where a machine breakdown is known in advance. Typical rescheduling methods usually re-assign or re-sequence jobs from a given schedule after machines break down. Recently, machine breakdowns can be forecasted with high accuracy before their actual occurrences from IoT sensors or artificial intelligence methods. We therefore define a new rescheduling problem in which jobs are re-assigned before machine breakdowns occur, and propose a mathematical programming model with three objective measures, makespan, stability and penalty cost. We then develop a simulated annealing (SA) algorithm combined with a fuzzy logic controller for adjusting the parameters in SA. We demonstrate the performance of the proposed algorithm with extensive experiments.

Suggested Citation

  • Young-In Kim & Hyun-Jung Kim, 2021. "Rescheduling of unrelated parallel machines with job-dependent setup times under forecasted machine breakdown," International Journal of Production Research, Taylor & Francis Journals, vol. 59(17), pages 5236-5258, September.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:17:p:5236-5258
    DOI: 10.1080/00207543.2020.1775910
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    Cited by:

    1. Ajay Surendrarao Bhongade & Prakash Manohar Khodke & Ateekh Ur Rehman & Manoj Dattatray Nikam & Prathamesh Dattatray Patil & Pramod Suryavanshi, 2023. "Managing Disruptions in a Flow-Shop Manufacturing System," Mathematics, MDPI, vol. 11(7), pages 1-22, April.

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