Risk measure of job shop scheduling with random machine breakdowns
Risk measure of job shop scheduling with random machine breakdowns
复制标题
随机机器故障车间调度的风险衡量
DOI：
10.1016/j.cor.2018.05.022
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发表时间：
2018-11
影响因子：
4.6
通讯作者：
Xiao Shichang
中科院分区：
文献类型：
--
作者：
Wu Zigao;Sun Shudong;Xiao Shichang
研究了随机机器故障下的车间作业调度问题，旨在寻找一种有效的评价和最小化调度风险的方法，以优化调度最大完工时间的稳定性.基于总松弛时间、资源块发生概率和停机时间与完工时间延迟之间的内在联系，提出了一种新的综合进度风险评价方法。由于它没有一个精确的解决方案，一个解析的近似方法被开发用于实际计算。在此基础上，采用遗传算法对进度风险进行优化。提供了21个基准JSP与RMBs的实验。结果表明，解析近似法和蒙特卡罗模拟法在进度风险优化方面的性能相当，但前者的计算速度要比后者快得多。与最先进的替代鲁棒性措施，这证实了所提出的措施进度风险评估的优越性进行了彻底的比较。
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 JSPs 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.
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影响因子：
--
作者：
Selcuk Goren;I. Sabuncuoglu
通讯作者：
Selcuk Goren;I. Sabuncuoglu
影响因子：
2.5
作者：
E. Ziegel
通讯作者：
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DOI：
10.1201/b17767-22
发表时间：
2012-08
期刊：
Actuarial Mathematics for Life Contingent Risks
影响因子：
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DOI：
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发表时间：
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期刊：
Comput. Ind. Eng.
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影响因子：
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