A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly

Abstract

ABSTRACTMost existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.KEYWORDS: Hybrid production line balancingdisassembly and assemblycycle timeworkload smoothnesshybrid genetic simulated annealing Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData will be made available on request.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Project (No. 51705386) and by China Scholarship Council (No. 201606955091).Notes on contributorsXiang SunXiang Sun received the B.Eng degree from Huazhong Agricultural University, Wuhan, China, in 2018. He is pursuing the Ph.D. degree at Wuhan University of Technology, Wuhan, China. His current research interests include manufacturing scheduling, machine learning and intelligent optimization algorithms.Shunsheng GuoShunsheng Guo received the B.Sc. degree in Mechanical manufacturing and automation from Huazhong University of Science and Technology, Wuhan, China, in 1984 and the Ph.D. degree in Mechanical Design and Theory from Wuhan University of Technology, Wuhan, China, in 2001. He is currently a Professor with the School of Mechanical and Electronic Engineering, Wuhan, China. His current research interests include manufacturing informatization and intelligent manufacturing.Jun GuoJun Guo received the M.S. degree (2009) and Ph.D. degree (2012) in Mechanical Engineering from Wuhan University of Technology, Wuhan, China. He is currently an Associate Professor with the School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China. His current research interests include production scheduling and optimization.Baigang DuBaigang Du received the M.S. degree (2013) and Ph.D. degree (2015) in Mechanical Engineering from Wuhan University of Technology, Wuhan, China. He is currently an Associate Professor with the School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China. His current research interests include manufacturing informatization and optimization modeling.Zhijie YangZhijie Yang received the M.Eng degree from Wuhan University of Technology, China, in 2015. He is pursuing the Ph.D. at Wuhan University of Technology, Wuhan, China. His current research interests include modern manufacturing integration and information systems.Kaipu WangKaipu Wang received his Ph.D. degree in the School of Mechanical Science and Engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2022. He was also a visiting scholar in the Department of Industrial Engineering & Innovation Sciences at Eindhoven University of Technology, the Netherlands, in 2021. He is currently an associate research fellow with the School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China. His research mainly focuses on industrial engineering, production planning and scheduling, and intelligent optimization.