Resilient and Green Supply Chain Management in the Circular Economy

Abstract

This study proposes a novel mathematical model for designing a resilient and eco-friendly supply chain network under uncertainty. The proposed model applies the life cycle assessment (LCA) methodology and the cap-and-trade mechanism to decrease carbon emissions. To address demand uncertainty, the chance-constrained programming (CCP) method is utilized. The proposed model is validated by solving thirty-three test problems and comparing the results with a non-resilient model without pandemic considerations. Results demonstrate that the total costs of the resilient model during the pandemic exceed those of the non-resilient model, emphasizing the necessity for decision-makers to balance the benefits of a resilient supply chain network with the associated costs. Furthermore, total emissions of the resilient model are lower, highlighting the environmental advantages of resilience. Comparing circular and linear models, the circular supply chains represent an 8.8% cost reduction and a 12.1% emission reduction that is attributed to the recycling of returned items.

Keywords: cap-and-trade policy, circular economy, green supply chains, mathematical modelling, supply chain resilience