A Robust Optimization Model for Designing of Green Close Loop Supply Chain of Perishable Products

Air pollution and CO2 emissions are important global issues, and reducing carbon emissions is one of the most important goals. In the supply chain of perishable products, delivery and distribution are significant issues, which many researchers have always paid attention to. In this research, we have used mixed integer linear programming to model the green supply chain, which includes two objective functions profit maximization and CO2 emission minimization. Due to the occurrence of uncertainties, cost and price are uncertain, we have used the robust optimization approach, and presented a new robust optimization model. This problem is included in NP-hard problems and the exact method is not efficient in large dimensions. The exact solution method of the constraint epsilon, and for the large-scale problems, meta-heuristic algorithms of NSGA, MOSA and MOPSO were used. In order to obtain the best and most accurate solutions in meta-heuristic algorithms, the parameters were adjusted with the help of Taguchi's design. According to the results of validity tests, the average difference with the optimal solution is 0.2 to 0.8% and in robust models, it is 0.0.7 to 0.9%, which is acceptable. The lowest execution time is related to the MOSA algorithm, NSGA and MOPSO respectively, and the efficiency of these algorithms is not significantly different according to the standard multi-objective criteria.