Abstract 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.

中文翻译：

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优化作业车间调度中的完工时间和稳定性风险

摘要 在现实世界的制造环境中，计划的执行经常会遇到不确定事件，这会带来性能恶化和生产系统不稳定的风险。本研究解决了随机机器故障下作业车间调度的性能和稳定性风险的优化，其中同时考虑了三个目标：完工期、完工期风险和稳定性风险。将提出有限预测完工时间下的缓冲方法并用于生成预测时间表，这允许插入额外的空闲时间来控制风险。通过利用有关风险和随机机器故障之间关系的可用信息，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的需求，开发了一种具有所提出的缓冲策略的多目标预测调度算法来生成帕累托解集。大量的实验结果表明，与现有方法相比，所提出的方法在多样性和收敛性方面都可以提供更好的帕累托解集。