Green closed-loop supply chain network design with stochastic demand: A novel accelerated benders decomposition method

Abstract

Changing the structure of supply chains to move towards less polluting industries and better performance has attracted many researchers in recent studies. Design of such networks is a process associated with uncertainties and control of the uncertainties during decision-making is of particular importance. In this paper, a two-stage stochastic programming model is presented for the design of a green closed-loop supply chain network. In order to reach the environmental goals, an upper bound of emission capability that would help governments and industries to control greenhouse gas emissions was considered. During the reverse logistics of this supply chain, waste materials were returned to the forward flow by the disassembly centers. To control the uncertainty of strategic decisions, demand and the upper bound of emission capacity with three possible scenarios were considered. To solve the model, a new accelerated Benders decomposition algorithm along with Pareto-optimal-cut was used. The efficiency of the proposed algorithm was compared with the regular Benders algorithm. The effect of different numerical values of parameters and probabilities of scenarios on the total cost was also examined.