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Profit-oriented partial disassembly line design: dealing with hazardous parts and task processing times uncertainty

Author

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  • Mohand Lounes Bentaha
  • Alexandre Dolgui
  • Olga Battaïa
  • Robert J. Riggs
  • Jack Hu

Abstract

This paper addresses the problem of profit-oriented disassembly line design and balancing considering partial disassembly, presence of hazardous parts and uncertainty of task processing times. Few papers have studied the stochastic disassembly line balancing problem and existing approaches have focused on heuristic and metaheuristic methods. Most existing work has concentrated on complete disassembly where task times are assumed to be normal random variables and where AND/OR graphs are not considered. The objective of this paper is the design of a serial line that obtains the maximum revenue and then balances the workload under uncertainty. The processing time of a disassembly task is assumed to be a random variable with any known probability distribution. An AND/OR graph is used to model the precedence relationships among tasks. Stochastic programming models and exact-based solution approaches combining the L-shaped algorithm and Monte Carlo sampling techniques are proposed. The relevance and applicability of the proposed models and solution methods are shown by solving efficiently a set of disassembly problem instances from the literature.

Suggested Citation

  • Mohand Lounes Bentaha & Alexandre Dolgui & Olga Battaïa & Robert J. Riggs & Jack Hu, 2018. "Profit-oriented partial disassembly line design: dealing with hazardous parts and task processing times uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 56(24), pages 7220-7242, December.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:24:p:7220-7242
    DOI: 10.1080/00207543.2017.1418987
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    Cited by:

    1. Zepeng Chen & Lin Li & Xiaojing Chu & Fengfu Yin & Huaqing Li, 2024. "Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process," Sustainability, MDPI, vol. 16(3), pages 1-23, January.
    2. Peng Hu & Feng Chu & Yunfei Fang & Peng Wu, 2022. "Novel distribution-free model and method for stochastic disassembly line balancing with limited distributional information," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1423-1446, July.
    3. Liang, Wei & Zhang, Zeqiang & Yin, Tao & Zhang, Yu & Wu, Tengfei, 2023. "Modelling and optimisation of energy consumption and profit-oriented multi-parallel partial disassembly line balancing problem," International Journal of Production Economics, Elsevier, vol. 262(C).
    4. Ömer Faruk Yılmaz & Büşra Yazıcı, 2022. "Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches," Annals of Operations Research, Springer, vol. 319(2), pages 1793-1843, December.
    5. Qi Zhang & Yang Xing & Man Yao & Jiacun Wang & Xiwang Guo & Shujin Qin & Liang Qi & Fuguang Huang, 2024. "An Improved Discrete Bat Algorithm for Multi-Objective Partial Parallel Disassembly Line Balancing Problem," Mathematics, MDPI, vol. 12(5), pages 1-22, February.
    6. Wanlin Yang & Zixiang Li & Chenyu Zheng & Zikai Zhang & Liping Zhang & Qiuhua Tang, 2024. "Multi-Objective Optimization for a Partial Disassembly Line Balancing Problem Considering Profit and Carbon Emission," Mathematics, MDPI, vol. 12(8), pages 1-19, April.
    7. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    8. Junkai He & Feng Chu & Feifeng Zheng & Ming Liu, 2021. "A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times," Annals of Operations Research, Springer, vol. 296(1), pages 71-93, January.
    9. Bentaha, Mohand-Lounes & Voisin, Alexandre & Marangé, Pascale, 2020. "A decision tool for disassembly process planning under end-of-life product quality," International Journal of Production Economics, Elsevier, vol. 219(C), pages 386-401.
    10. Yicong Gao & Shanhe Lou & Hao Zheng & Jianrong Tan, 2023. "A data-driven method of selective disassembly planning at end-of-life under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 565-585, February.
    11. 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.
    12. Fang, Yilin & Liu, Quan & Li, Miqing & Laili, Yuanjun & Pham, Duc Truong, 2019. "Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations," European Journal of Operational Research, Elsevier, vol. 276(1), pages 160-174.

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