Document Type : Article
Faculty of Industrial Engineering- West Tehran Branch, Islamic Azad University
Faculty of Industrial Engineering Bu-Ali Sina University, Hamedan
In this paper, a new multi-objective non-linear mixed-integer mathematical programming model is presented. The goals are to optimize the problem with respect to economic, social, and qualitative viewpoints, simultaneously. In the literature, there is a supply chain management network form as two parts: The forward logistic and the reverse logistic and their combinations are called the closed-loop supply chain and the reverse supply chain includes some parts such as collection center, recycle center, guarantee and disposal center with respect to the objective of minimizing the waste amount of materials and products in the supply chain. In general, the closed-loop supply chain can follow the economic, competitive, environmental and social situation to achieve its goals. In forward direct, we consider suppliers, plants, distributors, and first market; while, in reverse one, guarantee, collection, recycling,
disposal, redistribution and second \ market centers, are \ applied. Moreover, distributors \ meet the \ customer's demand based on traditional and internet sales. Therefore, a stochastic multi-objective multi-product closed-loop supply chain network model is designed. In this manner, the capacity of facilities and location decisions are considered. In economic objective part, the logic of internet sales is relied on customer behavior strategy. Seen from other way round, the employment's profit of employees is considered in both economic and, in turn, social responsibility. In fact, in this proposed model, distributors meet the customer's needs by internet sale or normal sale which this concept is used in supply chain's structure. Furthermore, in reverse logistic there are found environmental and disposal concepts in decreasing \ defective area, analyzed. With this \ complexity \ regard of this \ model, the multi-objective GRASP heuristic algorithm to solve the model is presented. In order to demonstrate applicability of the proposed model, comparisons are analyzed and compared with one of the most developed multi-objective evolutionary algorithm called non-dominated sorting genetic algorithm (NSGA-II). The results demonstrated that the applicability of multi-objective GRASP algorithm in terms of convergent and spacing of Pareto solutions in comparison with another one to solve closed-loop supply chain problem.