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A Balancing Method of Mixed-model Disassembly Line in Random Working Environment

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  • Xuhui Xia

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Wei Liu

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Zelin Zhang

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Lei Wang

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Jianhua Cao

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Xiang Liu

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China)

Abstract

Disassembly is a necessary link in reverse supply chain and plays a significant role in green manufacturing and sustainable development. However, the mixed-model disassembly of multiple types of retired mechanical products is hard to be implemented by random influence factors such as service time of retired products, degree of wear and tear, proficiency level of workers and structural differences between products in the actual production process. Therefore, this paper presented a balancing method of mixed-model disassembly line in a random working environment. The random influence of structure similarity of multiple products on the disassembly line balance was considered and the workstation number, load balancing index, prior disassembly of high demand parts and cost minimization of invalid operations were taken as targets for the balancing model establishment of the mixed-model disassembly line. An improved algorithm, adaptive simulated annealing genetic algorithm (ASAGA), was adopted to solve the balancing model and the local and global optimization ability were enhanced obviously. Finally, we took the mixed-model disassembly of multi-engine products as an example and verified the practicability and effectiveness of the proposed model and algorithm through comparison with genetic algorithm (GA) and simulated annealing algorithm (SA).

Suggested Citation

  • Xuhui Xia & Wei Liu & Zelin Zhang & Lei Wang & Jianhua Cao & Xiang Liu, 2019. "A Balancing Method of Mixed-model Disassembly Line in Random Working Environment," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2304-:d:223556
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    References listed on IDEAS

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    1. Mohand Lounes Bentaha & Olga Battaïa & Alexandre Dolgui, 2015. "An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 53(6), pages 1807-1818, March.
    2. Minjung Kwak, 2018. "Optimal Line Design of New and Remanufactured Products: A Model for Maximum Profit and Market Share with Environmental Consideration," Sustainability, MDPI, vol. 10(11), pages 1-15, November.
    3. Bingtao Hu & Yixiong Feng & Hao Zheng & Jianrong Tan, 2018. "Sequence Planning for Selective Disassembly Aiming at Reducing Energy Consumption Using a Constraints Relation Graph and Improved Ant Colony Optimization Algorithm," Energies, MDPI, vol. 11(8), pages 1-18, August.
    4. Can B. Kalayci & Olcay Polat & Surendra M. Gupta, 2016. "A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem," Annals of Operations Research, Springer, vol. 242(2), pages 321-354, July.
    5. F. Tao & Y. Cheng & L. Zhang & A. Y. C. Nee, 2017. "Advanced manufacturing systems: socialization characteristics and trends," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1079-1094, June.
    6. Yicong Gao & Qirui Wang & Yixiong Feng & Hao Zheng & Bing Zheng & Jianrong Tan, 2018. "An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line," Energies, MDPI, vol. 11(5), pages 1-18, May.
    7. Jia Liu & Shuwei Wang, 2017. "Balancing Disassembly Line in Product Recovery to Promote the Coordinated Development of Economy and Environment," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    8. McGovern, Seamus M. & Gupta, Surendra M., 2007. "A balancing method and genetic algorithm for disassembly line balancing," European Journal of Operational Research, Elsevier, vol. 179(3), pages 692-708, June.
    9. Qi Wang & Dunbing Tang & Shipei Li & Jun Yang & Miguel A. Salido & Adriana Giret & Haihua Zhu, 2019. "An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products," Sustainability, MDPI, vol. 11(2), pages 1-22, January.
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    Cited by:

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    2. Junyong Liang & Shunsheng Guo & Yunfei Zhang & Wenfang Liu & Shengwen Zhou, 2021. "Energy-Efficient Optimization of Two-Sided Disassembly Line Balance Considering Parallel Operation and Uncertain Using Multiobjective Flatworm Algorithm," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    3. Jianhua Cao & Xuhui Xia & Lei Wang & Zelin Zhang & Xiang Liu, 2019. "A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    4. Lixia Zhu & Zeqiang Zhang & Yi Wang & Ning Cai, 2020. "On the end-of-life state oriented multi-objective disassembly line balancing problem," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1403-1428, August.
    5. Masood Fathi & Amir Nourmohammadi & Morteza Ghobakhloo & Milad Yousefi, 2020. "Production Sustainability via Supermarket Location Optimization in Assembly Lines," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
    6. He, Junkai & Chu, Feng & Dolgui, Alexandre & Anjos, Miguel F., 2024. "Multi-objective disassembly line balancing and related supply chain management problems under uncertainty: Review and future trends," International Journal of Production Economics, Elsevier, vol. 272(C).

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