Journal
COMPUTERS & OPERATIONS RESEARCH
Volume 99, Issue -, Pages 1-12Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2018.05.022
Keywords
Scheduling; job shop; Schedule risk; Risk measure; Machine breakdown
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Funding
- National Natural Science Foundation of China [51475383]
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Job shop scheduling problem (JSP) with random machine breakdowns (RMBs) is studied in this paper for the purpose of finding an efficient measure for assessing and minimizing the schedule risk to optimize the stability of the schedule makespan. A novel and comprehensive measure for schedule risk evaluation is proposed based on the internal relation among the total slack time, the probability and downtime of RMBs, and the makespan delay. Since it does not come with an exact solution, an analytical approximation method is developed for practical calculation. Based on this method, the genetic algorithm is used to minimize the schedule risk. Experiments of twenty-one benchmark ISPs with RMBs are provided. Results show that while both the analytical approximation method and the Monte Carlo simulation perform similarly in the optimization of the schedule risk, the former computes much faster than the latter. Thorough comparison is also made with the state-of-the-art surrogate robustness measures, which confirms the superiority of the proposed measure for schedule risk evaluation. (C) 2018 Elsevier Ltd. All rights reserved.
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