An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems
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...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/2/256 |
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| author | Xinshuo Cui Qingbo Meng Jiacun Wang Xiwang Guo Peisheng Liu Liang Qi Shujin Qin Yingjun Ji Bin Hu |
| author_facet | Xinshuo Cui Qingbo Meng Jiacun Wang Xiwang Guo Peisheng Liu Liang Qi Shujin Qin Yingjun Ji Bin Hu |
| author_sort | Xinshuo Cui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-65cc88f9adea472cafbd81d1a88d2c93 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-65cc88f9adea472cafbd81d1a88d2c932025-01-24T13:39:54ZengMDPI AGMathematics2227-73902025-01-0113225610.3390/math13020256An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing ProblemsXinshuo Cui0Qingbo Meng1Jiacun Wang2Xiwang Guo3Peisheng Liu4Liang Qi5Shujin Qin6Yingjun Ji7Bin Hu8College of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, ChinaCollege of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, ChinaDepartment of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ 07764, USACollege of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, ChinaCollege of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, ChinaDepartment of Computer Science and Technology, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Economics and Management, Shangqiu Normal University, Shangqiu 476000, ChinaFaculty of Information, Liaoning University, Shenyang 110036, ChinaDepartment of Computer Science and Technology, Kean University, Union, NJ 07083, USAIn 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.https://www.mdpi.com/2227-7390/13/2/256disassembly line balancingdisassembly sequencesustainabilitycarbon savingsdiscrete whale optimization algorithm |
| spellingShingle | Xinshuo Cui Qingbo Meng Jiacun Wang Xiwang Guo Peisheng Liu Liang Qi Shujin Qin Yingjun Ji Bin Hu An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems Mathematics disassembly line balancing disassembly sequence sustainability carbon savings discrete whale optimization algorithm |
| title | An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems |
| title_full | An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems |
| title_fullStr | An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems |
| title_full_unstemmed | An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems |
| title_short | An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems |
| title_sort | evolutionary learning whale optimization algorithm for disassembly and assembly hybrid line balancing problems |
| topic | disassembly line balancing disassembly sequence sustainability carbon savings discrete whale optimization algorithm |
| url | https://www.mdpi.com/2227-7390/13/2/256 |
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