Designing a Sustainable Closed-Loop Supply Chain Considering Carbon Tax and Technology Selection in the Battery Industry

Over the past decade, growing environmental concerns and social legislations have forced decision makers to design their supply chains with regard to the environmental impacts, responsiveness, and economic benefits. In this research, a mixed integer linear programming (MILP)  model is developed to optimize the objectives of cost, environmental impacts and responsiveness in order to determine appropriate policies about the decisions of carbon tax, technology selection, location, capacity of facilities. In this model, there are simultaneously two types of disruption and operational risk for some parameters such as demand and costs, which is named hybrid uncertainty. To cope with this type of uncertainty, a possibilistic stochastic programming approach using concepts of credibility constraint programming is proposed to design a closed-loop supply chain. A high-performance flexible programming approach is applied to solve the multi-objective model. Results show that the appropriate rate of carbon tax is around 20000 Rial per kilogram of carbon. The results indicate that in order to optimize the level of responsiveness and reduce the environmental impacts and costs, it is better to use the green technologies in the plants and recycling centers and to establish the plant and distribution centers near the customer zones.