Robust optimisation for ripple effect on reverse supply chain: an industrial case study
Gökhan Özçelik,
Ömer Faruk Yılmaz and
Fatma Betül Yeni
International Journal of Production Research, 2021, vol. 59, issue 1, 245-264
Abstract:
This study examines the ripple effect on the system performance of the reverse supply chain (RSC) network and introduces a robust optimisation model for designing strong RSC networks to cope with the uncertainties caused by the ripple effect. In this manner, to the best knowledge of authors, a robust optimisation model for RSC design against the ripple effect in the context of green principles is formulated for the first time. That being the case, the study aims to provide remarkable managerial insights thanks to the developed robust optimisation model by adopting a proactive strategy before a long-term disruption occurs in the network. To this end, the robust optimisation model is applied to an industrial case study from an enterprise disassembling the household appliance. The scope of the case study is limited to the enterprise's recycling activities in the northern region of Turkey which is a potential landslide site due to the heavy rainfall. Computational experiments are performed through a set of scenarios regarding the different weight uncertainty values to reveal the changes in objective function value and decision variables. Based on the results, whilst the computationally tractable robust solutions are obtained; the price of robustness is higher than expected to protect the constraints against violation when the probability of constraint violation equals 0.01.
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2020.1740348 (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:59:y:2021:i:1:p:245-264
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20
DOI: 10.1080/00207543.2020.1740348
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 ().