Capacitated disassembly scheduling with random demand and operation time

Fuli Zhou, Yandong He, Panpan Ma, Ming K. Lim and Saurabh Pratap

Journal of the Operational Research Society, 2022, vol. 73, issue 6, 1362-1378

Abstract: The disassembly activity, regarding as the crucial stage in recycling operations, has attracted increasing focus owing to the significance of eco-economics and environmental issues. This paper examines the capacitated disassembly scheduling with demand and disassembly operation time uncertainty consideration, which is the problem of determining the quantity of the end-of-life (EOL) products (root item) to be disassembled while satisfying recycling market. The addressed problem is formulated as a novel stochastic programming model and a hybrid genetic-based algorithm (HGA) is proposed to derive the best solution. To deal with the uncertain demand of disassembled parts/modules (leaf item) and the disassembly operation time, the fixed sample size (FSS) sampling strategy is employed and embedded into the designed heuristic algorithm, lunched by the Monte Carlo Simulation. The numerical instances under different scales are performed, and results show that the developed HGA manifests good performance in terms of accuracy and efficiency.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2021.1911603 (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:tjorxx:v:73:y:2022:i:6:p:1362-1378

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2021.1911603

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

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