Scenario-based multi-objective optimisation model based on supervised machine learning to configure a plastic closed-loop supply chain network

Sahand Ashtab and Babak Mohamadpour Tosarkani

International Journal of Business Performance and Supply Chain Modelling, 2023, vol. 14, issue 1, 106-128

Abstract: Plastic recycling has received a lot of attention around the world. In this regard, a multi-objective optimisation model for plastic closed loop supply chain (CLSC) configuration is developed. Specifically, this paper simultaneously investigates the impact of adding washing machines to plastic recovery centres and corporations' role in consumer awareness on plastic recycling on plastic CLSC network configuration cost and carbon dioxide (i.e., CO₂) emissions. Our numerical results indicate that the combination of adding washing machines to recovery centres, and increased return of plastic products due of increased corporate responsibility in consumer awareness have the potential to contribute to both economic and environmental pillars of sustainability by decreasing the design cost, i.e., by 3.93%, and CO₂ emissions, i.e., by 14.24%. Furthermore, sensitivity analysis is conducted to consider the effects of unpredictable changes in demand and return. The implications of our study concerning social sustainability, policymakers, and municipalities are discussed.

Keywords: multi-objective optimisation; machine learning technique; logistic regression; corporate responsibility; closed loop supply chain; CLSC; plastic. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=130469 (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:ids:ijbpsc:v:14:y:2023:i:1:p:106-128

Access Statistics for this article

More articles in International Journal of Business Performance and Supply Chain Modelling from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().