Reinforcement Learning for Selective Disassembly System Optimization Problems: A Survey

Abstract

Survey/review study Reinforcement Learning for Disassembly System Optimization Problems: A Survey Xiwang Guo 1,2,*, Zhiliang Bi 2, Jiacun Wang 1, Shujin Qin 3, Shixin Liu 4, and Liang Qi 5 1 Department of Computer Science and Software Engineering, Monmouth University, New Jersey 07710, USA 2 Department of Information Control, Liaoning Petrochemical University, Fushun 113005, China 3 Department of Economic Management, Shangqiu Normal University, Shangqiu 476000, China 4 Department of Information Science and Engineering, Northeast University, Shenyang 110819, China 5 Department of Computer Intelligence Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China * Correspondence: x.w.guo@163.com     Received: 16 October 2022 Accepted: 28 November 2022 Published:    Abstract: The disassembly complexity of end-of-life products increases continuously. Traditional methods are facing difficulties in solving the decision-making and control problems of disassembly operations. On the other hand, the latest development in reinforcement learning makes it more feasible to solve such kind of complex problems. Inspired by behaviorism psychology, reinforcement learning is considered as one of the most promising directions to achieve universal artificial intelligence (AI). In this context, we first review the basic ideas, mathematical models, and various algorithms of reinforcement learning. Then, we introduce the research progress and application subjects in the field of disassembly and recycling, such as disassembly sequencing, disassembly line balancing, product transportation, disassembly layout, etc. In addition, the prospects, challenges and applications of reinforcement learning based disassembly and recycling are also comprehensively analyzed and discussed.