Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry

Abstract Today, due to the increasing environmental hazards and governmental regulations, as well as the limitation of sources of production, researchers have paid special attention to the design of closed-loop green supply chain networks. The closed-loop supply chain networks (CLSCN) include the returns processes and the producers aim to capturing additional value considering further integration of all supply chain activities. Therefore, all return processes need to be optimized as well as considering environmental impacts leading to form a closed-loop green supply chain network (CLGSCN). For decision making purposes, operational and tactical decision making levels are integrated to configure a coordinated supply chain network aiming to maximize profit while keeping environmental-friendly policies. The case is more sophisticated in melting industries where the collection and categorization in return process and different environmental challenges should be considered at the same time. Thus, in this paper, a CLGSCN of a melting industry is modeled with respect to environmental hazards to optimiza overall profits. Since real-world demand in melting industry under study is uncertain, the robust optimization has been employed, and while the optimization of the proposed mathematical model is time consuming, an improved version of the genetic algorithm has been implemented as a solution method. This study has been carried out at Melting Imen Tabarestan (MIT) company in Iran. The proposed model along with the solution method are investigated in the case study. The results imply the effectiveness and applicability of the model and provide tactical considerations for the managers and practitioners.