a multi-objective closed-loop supply chain network model with location decisions

In this paper, we have developed a mathematical model to design a closed-loop supply chain of a car manufacturing company operating in a build-to-order environment under various sources of uncertainty. the aim is to formulate a proposed mathematical model for multi-product, multi-period, multi-echelon scm of a build-to-order system which integrates suppliers, plants, manufacturers and customer zones (sales representatives) using the mixed-integer nonlinear programming (minlp) approach under uncertainty of demand, shortage, quantity of returned product, transport, product, relevant costs and lead time. the objectives are to minimize the total cost and weighted activity days and to maximize the average service level, forward and reverse responsiveness of the network. to demonstrate the applicability of the model, 19 test problems with one case study in car manufacturing are used. since the proposed model is np-hard, to solve the problem, a pareto-based approach based non-dominated sorting genetic algorithm (nsga-ii) and non-dominated ranking genetic algorithm (nrga) applied. the results showed the applicability of the proposed problem in real life stations to find and manage optimal solutions.