Multi-objective robust optimization model for social responsible closed-loop supply chain solved by non-dominated sorting genetic algorithm

In this study a supply chain network design model has been developed considering both forward and reverse flows through the supply chain. Total Cost, environmental factors such as CO2 emission, and social factors such as employment and fairness in providing job opportunities are considered in three separate objective functions. The model seeks to optimize the facility location problem along with determining network flows, type of technology, and capacity of manufacturers. Since the customer’s demand is tainted with high degree of uncertainty, a robust optimization approach is proposed to deal with this important issue. An efficient genetic algorithm is applied to determine the Pareto optimal solutions. Finally, a case study is conducted on steel industry to evaluate the efficiency of the developed model and solution algorithm.