Sample average approximation for multi-vehicle collection–disassembly problem under uncertainty

Muh. Khoirul Khakim Habibi, Olga Battaïa, Van-Dat Cung, Alexandre Dolgui and Manoj Kumar Tiwari

International Journal of Production Research, 2019, vol. 57, issue 8, 2409-2428

Abstract: The implementation of the circular economy is increasingly supported by many governments. It is performed by integrating the activities of reverse supply chain (RSC) into those of forward supply chain. However, many companies that traditionally focus on the activities of forward supply chain have decided to collaborate with third-party reverse logistics providers to manage the RSC. This collaboration motivates the work presented in this paper to propose better decisions for decision makers in the providers under the fact that integrating decisions of the collection of End-of-Life products and their disassembly process proposes a RSC with better performance. In this paper, an integrated problem concerning those decisions is presented and formalised. It also deals with the uncertainty of the quality and the quantity of products as well as the demands of the associated components. Two approximate methods are developed to provide the solutions.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2018.1519262 (text/html)
Access to full text is restricted to subscribers.

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:taf:tprsxx:v:57:y:2019:i:8:p:2409-2428

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2018.1519262

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().