The following article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). In recent years, there has been extensive research on DRL techniques, but without considering realistic, flexible and human-centered shopfloors. A research gap can be identified in the context of make-to-order oriented discontinuous manufacturing as it is often represented in medium-size companies with high service levels. From practical industry projects in this domain, we recognize requirements to depict flexible machines, human workers and capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-depended setup times and (partially) automated tasks. On the other hand, intensive research has been done on metaheuristics in the context of DRC-FJSSP. However, there is a lack of suitable and generic scheduling methods that can be holistically applied in sociotechnical production and assembly processes. In this paper, we first formulate an extended DRC-FJSSP induced by the practical requirements mentioned. Then we present our proposed hybrid framework with parallel computing for multicriteria optimization. Through numerical experiments with real-world data, we confirm that the framework generates feasible schedules efficiently and reliably. Utilizing DRL instead of random operations leads to better results and outperforms traditional approaches.

翻译：本文提出了一种结合深度强化学习（DRL）的模因算法，用于解决面向实际的双资源约束柔性作业车间调度问题（DRC-FJSSP）。近年来，尽管对DRL技术进行了广泛研究，但尚未考虑现实、灵活且以人为中心的车间环境。在面向订单生产的非连续制造场景中（常见于高服务水平的中型企业），存在研究空白。基于该领域的实际工业项目，我们认识到需刻画以下需求：柔性机器、人类工人及其技能、装夹与加工作业、物料到达时间、面向物料清单（BOM）制造的并行任务复杂工件路径、序列依赖的装夹时间及（部分）自动化任务。另一方面，尽管元启发式方法在DRC-FJSSP领域已有深入研究，但缺乏能在社会技术生产与装配过程中整体应用的通用调度方法。本文首先基于上述实际需求，构建了扩展的DRC-FJSSP问题模型；随后提出了一种结合并行计算的多准则优化混合框架。通过使用真实数据的数值实验，我们证实该框架能够高效且可靠地生成可行调度方案。利用DRL替代随机操作可获得更优结果，并超越传统方法。