Design of closed-loop supply chain network with considering of multi-part centers under uncertainty with two robust and stochastic approaches

An appropriate supply chain is consisting of foreign suppliers, facilities of productions, distribution centers, sales, demands and transportation. A system may be used for both reducing costs in supply chain and helping to respond customers' demands quickly. In the present study, designing a closed-loop supply chain as strategic decision considered, and as integrate designing of direct-reverse logistic system cause to prevent sub-optimization resulted by designing separated to systems, and closed-loop model used. The studied system in the research was modelling an integrated direct and reverse logistic system as multi surfaces, multi-objective, multi-production and multi-stages optimization through limited capacities and lack of assuring in demands, costs and return. So, in order to oppose lack of assuring, two stable and potential optimization strategies considered. Firstly, regarding to parameters such as demands, costs and potential return and using normal distribution, modelling suggested, and as potential strategy may loss efficiency in large sizes, stable optimization proposed to use, therefore, mix integer nonlinear programming model used, then its stable equivalent considered. The aim of present study is to minimize costs and increase quality of recyclable productions. Finally, two multi-objectives metaheuristic and genetic algorithms solved the problems through non-dominated sorting and particle swarm optimization algorithms, then the results compared. According to the obtained results, NSGA II is more suitable than MOPSO.