Optimizing makespan and stability risks in job shop scheduling
Optimizing makespan and stability risks in job shop scheduling
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DOI：
10.1016/j.cor.2020.104963
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Zigao Wu;Shudong Sun;Shaohua Yu
中科院分区：
文献类型：
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作者：
Zigao Wu;Shudong Sun;Shaohua Yu
在现实的制造环境中，计划的执行经常会遇到不确定事件，这会带来性能下降和生产系统不稳定的风险。本文研究随机机器故障情况下作业车间调度的性能和稳定性风险优化问题，同时考虑最大完工时间、最大完工时间风险和稳定性风险三个目标。提出了在有限预测完工时间下的缓冲方法，并将其用于生成预测计划，该计划允许插入额外的空闲时间来控制风险。通过利用风险与随机机器故障之间的关系的可用信息，我们开发了两种基于操作块的缓冲策略。为了满足具有不同风险偏好的决策者，提出了一种基于所提缓冲策略的多目标预测调度算法来生成Pareto解集。大量的实验结果表明，与现有方法相比，所提方法在多样性和收敛性方面都能提供更好的Pareto解集。
In real-world manufacturing environments, the execution of a schedule often encounters uncertain events, which will bring the risks of performance deterioration and production system instability. This study addresses the optimization of risks both in performance and stability for the job shop scheduling under random machine breakdowns, in which three objectives: makespan, makespan risk and stability risk are considered at the same time. The buffering approach under the limited predictive makespan will be proposed and used to generate predictive schedules, which allows inserting additional idle time to control the risks. By utilizing the available information about the relationship between the risks and the random machine breakdowns, we have developed two kinds of operation-block based buffering strategies. In order to meet the decision makers with different risk preferences, a multi-objective predictive scheduling algorithm with the proposed buffering strategies is developed to generate a Pareto solution set. Extensive experimental results indicate that, compared with the existing methods, the proposed method can provide a better Pareto solution set in terms of both the diversity and the convergence.