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A decision tool for disassembly process planning under end-of-life product quality

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  • Bentaha, Mohand-Lounes
  • Voisin, Alexandre
  • Marangé, Pascale

Abstract

One of the major sources of uncertainty in disassembly systems is the quality or states of post-consumer products. This paper develops a decision tool for disassembly process planning under variability of the End-of-Life product quality. The objective is to maximize the profit of the disassembly process. This latter is calculated as the difference between the revenue generated by recovered parts and the cost of the disassembly tasks. The revenue of a product (or a subassembly, or a component) depends on its quality. The proposed methodology helps to take decisions about the best disassembly process and the depth of disassembly, depending on the quality of the products to be disassembled. Industrial applicability and interest are shown using an industrial case focused on the remanufacturing of mechatronic parts in the automotive industry.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:219:y:2020:i:c:p:386-401
    DOI: 10.1016/j.ijpe.2019.07.015
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    References listed on IDEAS

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    1. Almalki, Saad J. & Nadarajah, Saralees, 2014. "Modifications of the Weibull distribution: A review," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 32-55.
    2. Ali Koc & Ihsan Sabuncuoglu & Erdal Erel, 2009. "Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph," IISE Transactions, Taylor & Francis Journals, vol. 41(10), pages 866-881.
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    4. 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.
    5. Bentaha, Mohand Lounes & Battaïa, Olga & Dolgui, Alexandre & Hu, S. Jack, 2015. "Second order conic approximation for disassembly line design with joint probabilistic constraints," European Journal of Operational Research, Elsevier, vol. 247(3), pages 957-967.
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    Cited by:

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    2. Bhattacharya, Sourabh & Govindan, Kannan & Ghosh Dastidar, Surajit & Sharma, Preeti, 2024. "Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).

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