This paper is concerned with stochastic scheduling of production and maintenance activities in a permutation flow shop setting. We present a two-stage stochastic mixed-integer program (SMIP) that adapts the conventional permutation flow shop scheduling problem for incorporating multiple preventive maintenance activities with various meter-based intervals. The model handles uncertainties in both processing times and the duration of maintenance activities. The concept of combining maintenance activities in scheduling problems is introduced and formulated, along with other practical considerations. The objective is to minimize the total expected cost associated with lateness penalties and maintenance resources. We use simulation–optimization (SO) for solving large-scale instances of the problem, and for validating the SMIP model. Through extensive computational experiments, we show that the SO method is superior in terms of efficiency and effectiveness and evaluate its sensitivity to the input data. Finally, a case study in earth-moving operations is presented, followed by managerial implications.

本文涉及在排列流水车间中生产和维护活动的随机调度。我们提出了一个两阶段的随机混合整数程序（SMIP），该程序适用于传统的置换流水车间调度问题，可以合并具有各种基于仪表间隔的多个预防性维护活动。该模型处理处理时间和维护活动持续时间方面的不确定性。引入并制定了将维护活动与调度问题相结合的概念，以及其他实际考虑因素。目的是最大程度地减少与延迟罚款和维护资源相关的总预期成本。我们使用仿真优化（SO）解决问题的大规模实例，并验证SMIP模型。通过大量的计算实验，我们证明了SO方法在效率和有效性方面是优越的，并评估了其对输入数据的敏感性。最后，介绍了在土方作业中的案例研究，以及管理意义。