Modeling and Solving Problem Sustainable Closed Loop Supply Chain Network Design for Petrochemical Products under Uncertainty Conditions

The petrochemical industry is one of the most important industries in the world where optimal management and decision-making in its activities will bring about great economic benefits as well as prosperity and development of related industries. This paper deals with the issue of petrochemical product supply chain management. A multi-objective optimization model is developed in which the strategic, long-term economic, social and environmental goals of the petrochemical industry are achieved. For this purpose, first using the Epsilon constrained evolution method, economic objective is considered as objective function and social and environmental goals are constrained as Epsilon. Then, the Pareto front is obtained from efficient solutions and in this front, the solution with the least deviation from the ideal is selected as the most efficient solution and recommended to industry managers. The data uncertainty in the proposed model is controlled using a robust feasibility planning approach. The numerical results show that not only the optimal fluctuation in the proposed robust approach is much less than the nominal value approach but it also significantly reduces the constraint flaw which reduces risk in decision making. In order to solve the proposed large-scale problem, the Banders decomposition method is applied based on the Epsilon multiple-constraint evolution method. Numerical results show that the proposed approach significantly improves the mean, standard deviation, and runtime in three quantitative measures and enables large-scale problem solving.