Stochastic Optimization of Resilient and Low-Carbon Reverse Supply Chain Under the Impacts of Pandemic

The increasing global warming and environmental concerns have brought a lot of attention to the optimization problem of low-carbon reverse supply chain network (RSCN). However, existing literature lack consideration for the risk factors associated with disruptions in optimization of low-carbon RSCN. Particularly under the unprecedented COVID-19 pandemic disruption impacts, RSCNs are facing numerous challenges in risk management. In this paper, we address the problem of designing a resilient and low-carbon RSCN under the impacts of pandemic disruption. We propose a two-stage stochastic programming (TSSP) model to minimize the total operating cost. The uncertain impacts of pandemic disruption on the supply capability of RSCN and the recovery process of capability in the post-disruption phase are taken into consideration. To enhance the resilience of the RSCN, we propose several strategies while considering the associated uncertainties. To tackle large-scale TSSP model, a decomposition-based tabu search (DTS) algorithm is designed. The results of numerical experiments indicate that the DTS algorithm outperforms the exact algorithm in terms of computational time and solution quality.