Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment.

对调度问题的研究对于工业工程师来说是一个常绿的挑战。数字技术的发展为实时收集和分析大量现场数据提供了可能，这为改进调度活动提供了宝贵的机会。因此，在不确定的情况下进行调度可能会受益于以下可能性：实时掌握工业设备的当前运行状况，并在制定最佳生产计划时将其考虑在内。为此，本文提出了适用于流水车间调度问题的鲁棒调度的模拟算法框架的概念验证。该框架由用于调度优化和离散事件模拟的遗传算法组成，并通过采用设备预测和健康管理（EPHM）模块的Digital Twin（DT）与现场同步。EPHM模块在基于DT的框架内的作用是利用数据驱动的统计模型对设备的故障概率进行实时计算，该模型将来自现场的传感器数据作为输入。在实验室环境中的流水车间应用中证明了该框架的可行性。