An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems

Xinshuo Cui, Qingbo Meng, Jiacun Wang, Xiwang Guo, Peisheng Liu (), Liang Qi, Shujin Qin, Yingjun Ji and Bin Hu ()
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Xinshuo Cui: College of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China
Qingbo Meng: College of Artificial Intelligence and Software, 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 Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China
Peisheng Liu: College of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China
Liang Qi: Department of Computer Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China
Shujin Qin: College of Economics and Management, Shangqiu Normal University, Shangqiu 476000, China
Yingjun Ji: Faculty of Information, Liaoning University, Shenyang 110036, China
Bin Hu: Department of Computer Science and Technology, Kean University, Union, NJ 07083, USA

Mathematics, 2025, vol. 13, issue 2, 1-23

Abstract: In order to protect the environment, an increasing number of people are paying attention to the recycling and remanufacturing of EOL (End-of-Life) products. Furthermore, many companies aim to establish their own closed-loop supply chains, encouraging the integration of disassembly and assembly lines into a unified closed-loop production system. In this work, a hybrid production line that combines disassembly and assembly processes, incorporating human–machine collaboration, is designed based on the traditional disassembly line. A mathematical model is proposed to address the human–machine collaboration disassembly and assembly hybrid line balancing problem in this layout. To solve the model, an evolutionary learning-based whale optimization algorithm is developed. The experimental results show that the proposed algorithm is significantly faster than CPLEX, particularly for large-scale disassembly instances. Moreover, it outperforms CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while maintaining high solution quality.

Keywords: disassembly line balancing; disassembly sequence; sustainability; carbon savings; discrete whale optimization algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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