A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

作者：Ling Wang, Zixiao Pan, Jingjing Wang · 发表于：Complex System Modeling and Simulation · 年份：2021 · DOI：10.23919/csms.2021.0027 · 被引用次数：293 · 研究领域：Scheduling and Optimization Algorithms、Elevator Systems and Control、Digital Transformation in Industry

As the critical component of manufacturing systems, production scheduling aims to optimize objectives in terms of profit, efficiency, and energy consumption by reasonably determining the main factors including processing path, machine assignment, execute time and so on. Due to the large scale and strongly coupled constraints nature, as well as the real-time solving requirement in certain scenarios, it faces great challenges in solving the manufacturing scheduling problems. With the development of machine learning, Reinforcement Learning (RL) has made breakthroughs in a variety of decision-making problems. For manufacturing scheduling problems, in this paper we summarize the designs of state and action, tease out RL-based algorithm for scheduling, review the applications of RL for different types of scheduling problems, and then discuss the fusion modes of reinforcement learning and meta-heuristics. Finally, we analyze the existing problems in current research, and point out the future research direction and significant contents to promote the research and applications of RL-based scheduling optimization.