A decision tool for disassembly process planning under end-of-life product quality
Mohand-Lounes Bentaha,
Alexandre Voisin and
Pascale Marangé
International Journal of Production Economics, 2020, vol. 219, issue C, 386-401
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.
Keywords: Sustainable manufacturing; Product recovery; Disassembly; Quality uncertainty management; Decision support system (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S092552731930252X
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:219:y:2020:i:c:p:386-401
DOI: 10.1016/j.ijpe.2019.07.015
Access Statistics for this article
International Journal of Production Economics is currently edited by Stefan Minner
More articles in International Journal of Production Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().