Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time

Ziyan Zhao, Pengkai Xiao, Jiacun Wang, Shixin Liu, Xiwang Guo, Shujin Qin and Ying Tang ()
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Ziyan Zhao: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Pengkai Xiao: Information and Control Engineering College, Liaoning Petrochemical University, Fushun 113001, China
Jiacun Wang: Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ 07764, USA
Shixin Liu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Xiwang Guo: Information and Control Engineering College, Liaoning Petrochemical University, Fushun 113001, China
Shujin Qin: College of Economics and Management, Shangqiu Normal University, Shangqiu 476000, China
Ying Tang: Electrical & Computer Engineering Department, Rowan University, Glassboro, NJ 08028, USA

Mathematics, 2023, vol. 12, issue 1, 1-19

Abstract: Disassembling discarded electrical products plays a crucial role in product recycling, contributing to resource conservation and environmental protection. While disassembly lines are progressively transitioning to automation, manual or human–robot collaborative approaches still involve numerous workers dealing with hazardous disassembly tasks. In such scenarios, achieving a balance between low risk and high revenue becomes pivotal in decision making for disassembly line balancing, determining the optimal assignment of tasks to workstations. This paper tackles a new disassembly line balancing problem under the limitations of quantified penalties for hazardous component disassembly and the switching time between adjacent tasks. The objective function is to maximize the overall profit, which is equal to the disassembly revenue minus the total cost. A mixed-integer linear program is formulated to precisely describe and optimally solve the problem. Recognizing its NP-hard nature, a metaheuristic algorithm, inspired by human idea generation and population evolution processes, is devised to achieve near-optimal solutions. The exceptional performance of the proposed algorithm on practical test cases is demonstrated through a comprehensive comparison involving its solutions, exact solutions obtained using CPLEX to solve the proposed mixed-integer linear program, and those of competitive peer algorithms. It significantly outperforms its competitors and thus implies its great potential to be used in practice. As computing power increases, the effectiveness of the proposed methods is expected to increase further.

Keywords: brain-storm optimization; clustering; disassembly line balancing problem; genetic operators; hazardous disassembly penalty; switching time (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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