An Optimized Advantage Actor-Critic Algorithm for Disassembly Line Balancing Problem Considering Disassembly Tool Degradation

Shujin Qin, Xinkai Xie, Jiacun Wang, Xiwang Guo, Liang Qi (), Weibiao Cai, Ying Tang and Qurra Tul Ann Talukder
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Shujin Qin: College of Economics and Management, Shangqiu Normal University, Shangqiu 476000, China
Xinkai Xie: College of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China
Jiacun Wang: Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ 07764, USA
Xiwang Guo: College of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China
Liang Qi: Department of Computer Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China
Weibiao Cai: College of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China
Ying Tang: College of Electrical and Computer Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Qurra Tul Ann Talukder: Department of Computer Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China

Mathematics, 2024, vol. 12, issue 6, 1-19

Abstract: The growing emphasis on ecological preservation and natural resource conservation has significantly advanced resource recycling, facilitating the realization of a sustainable green economy. Essential to resource recycling is the pivotal stage of disassembly, wherein the efficacy of disassembly tools plays a critical role. This work investigates the impact of disassembly tools on disassembly duration and formulates a mathematical model aimed at minimizing workstation cycle time. To solve this model, we employ an optimized advantage actor-critic algorithm within reinforcement learning. Furthermore, it utilizes the CPLEX solver to validate the model’s accuracy. The experimental results obtained from CPLEX not only confirm the algorithm’s viability but also enable a comparative analysis against both the original advantage actor-critic algorithm and the actor-critic algorithm. This comparative work verifies the superiority of the proposed algorithm.

Keywords: disassembly line balancing; tool deterioration; reinforcement learning; advantage actor-critic algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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