A Stochastic Closed-Loop Supply Chain Network Optimization Problem Considering Flexible Network Capacity

Nowadays, due to the concern of environmental challenges, global warming and climate change, companies across the globe have increasingly focused on the sustainable operations and management of their supply chains. Closed-loop supply chain (CLSC) is a new concept and practice, which combines both traditional forward supply chain and reverse logistics in order to simultaneously maximize the utilization of resource and minimize the generation of waste. In this paper, a stochastic CLSC network optimization problem with capacity flexibility is investigated. The proposed optimization model is able to appropriately handle the uncertainties from different sources, and the network configuration and decisions are adjusted by the capacity flexibility under different scenarios. The sample average approximation (SAA) method is used to solve the stochastic optimization problem. The model is validated by a numerical experiment and the result has revealed that the quality and consistency of the decision-making can be dramatically improved by modelling the capacity flexibility