IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v219y2020icp386-401.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S092552731930252X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2019.07.015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. Diefenbach, Johannes & Stolletz, Raik, 2022. "Stochastic assembly line balancing: General bounds and reliability-based branch-and-bound algorithm," European Journal of Operational Research, Elsevier, vol. 302(2), pages 589-605.
    8. 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).
    9. 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.
    10. 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.
    11. García-Villoria, Alberto & Corominas, Albert & Nadal, Adrià & Pastor, Rafael, 2018. "Solving the accessibility windows assembly line problem level 1 and variant 1 (AWALBP-L1-1) with precedence constraints," European Journal of Operational Research, Elsevier, vol. 271(3), pages 882-895.
    12. Szymkowiak, Magdalena & Iwińska, Maria, 2016. "Characterizations of Discrete Weibull related distributions," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 41-48.
    13. 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.
    14. Xiang Jia & Saralees Nadarajah & Bo Guo, 2020. "Inference on q-Weibull parameters," Statistical Papers, Springer, vol. 61(2), pages 575-593, April.
    15. Yolanda M. Gómez & Diego I. Gallardo & Carolina Marchant & Luis Sánchez & Marcelo Bourguignon, 2023. "An In-Depth Review of the Weibull Model with a Focus on Various Parameterizations," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    16. XiaoFei, Lu & Min, Liu, 2014. "Hazard rate function in dynamic environment," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 50-60.
    17. Hu, Qing-Mi & Hu, Shaolong & Wang, Jian & Li, Xiaoping, 2021. "Stochastic single allocation hub location problems with balanced utilization of hub capacities," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 204-227.
    18. Sulewski Piotr & Szymkowiak Magdalena, 2022. "The Weibull lifetime model with randomised failure-free time," Statistics in Transition New Series, Polish Statistical Association, vol. 23(4), pages 59-76, December.
    19. Ming Liu & Rongfan Liu & E Zhang & Chengbin Chu, 2022. "Eco-friendly container transshipment route scheduling problem with repacking operations," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1010-1035, July.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:219:y:2020:i:c:p:386-401. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.