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

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.