Designing a Sustainable Green Closed-Loop Supply Chain under Uncertainty and Various Capacity Levels

Mohsen Tehrani and Surendra M. Gupta
Additional contact information
Mohsen Tehrani: Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA
Surendra M. Gupta: Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA

Logistics, 2021, vol. 5, issue 2, 1-31

Abstract: The ever-increasing concerns of the growth in the volume of waste tires and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. In the real world, the CLSC network design is subject to a variety of uncertainties, such as random and fuzzy (epistemic) uncertainties. Designing a reliable and environmentally cautious CLSC with consideration of risks and the uncertainty of the parameters in the network is necessary for a successful supply-chain network. This study proposes a sustainable and environmentally cautious closed-loop supply-chain network for the tire industry, by considering several recovery options, including retreading, recycling, and energy recovery. This study aims to design and develop a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty. There are two types of uncertainties associated with the parameters in the network. There is an uncertainty associated with the demand, which is expressed in some future scenarios according to the probability of their occurrences, as well as fuzzy-based uncertainty associated with return rates, retreading rates, recycling rates, procurement, and production costs, which are expressed with possibilistic distributions. In order to deal with this hybrid uncertainty, a robust fuzzy stochastic programming approach has been proposed, and the proposed mixed integer programming model is applied to a case study in the tire industry to validate the model. The result indicates the applicability of the proposed model and its efficiency to control the hybrid uncertainties and the risk level in the network.

Keywords: closed-loop supply chain; location–allocation problem; robust optimization; tire industry; multi-objective decision making; hybrid uncertainty; augmented epsilon constraint (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2305-6290/5/2/20/pdf (application/pdf)
https://www.mdpi.com/2305-6290/5/2/20/ (text/html)

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:gam:jlogis:v:5:y:2021:i:2:p:20-:d:531771

Access Statistics for this article

Logistics is currently edited by Ms. Mavis Li

More articles in Logistics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().