Development of closed-loop supply chain mathematical model (cost-benefit-environmental effects) under uncertainty conditions by approach of genetic algorithm

In the current world, the debate on the reinstatement and reuse of consumer prod-ucts has become particularly important. Since the supply chain of the closed loop is not only a forward flow but also a reverse one; therefore, companies creating integ-rity between direct and reverse supply chain are successful. The purpose of this study is to develop a new mathematical model for closed loop supply chain net-work. In the real world the demand and the maximum capacity offered by the sup-plier are uncertain which in this model; the fuzzy theory discussion was used to cover the uncertainty of the mentioned variables. The objective functions of the model include minimizing costs, increasing revenues of recycling products, increas-ing cost saving from recycling and environmental impacts. According to the NP-hard, an efficient algorithm was suggested based on the genetic Meta heuristic algo-rithm to solve it. Twelve numerical problems were defined and solved using the NSGA-II algorithm to validate the model