An integrated dynamic model to locate a competitive closed-loop supply chain facility under conditions of uncertainty: A case study of the auto parts industry

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With the expansion of competitive markets, supply chain management has become one of the critical issues facing businesses. One of the advantages of sustainable competition for companies is to make supply chain activities more efficient and effective. This paper aims at an integrated closed-loop supply chain (CLSC) problem which is multi-objective, multi-product, multi-period, and multi-level with limited capacities and uncertain conditions of demand and return products. The proposed supply chain network consists of five levels in the forward flow. There are five centers in the backward flow as well. The purpose of this network is to determine the optimal number and location of facilities required in each period and the optimal amount of the transfer flow of products or raw materials through different transportation modes between facilities. In this proposed model, three objective functions are taken into consideration. The first one minimizes all the costs. The second objective function maximizes the quality of products. The third objective function seeks to minimize the sum of deviations from the ideal score of the principal component of each supplier. The data of this research are taken from Pishro Diesel Company. To solve the proposed problem, several methods and algorithms have been used, including unscaled goal programming, boundary objectives, three single-objective meta-heuristic algorithms (PSO, RDA, and TGA), and multi-objective meta-heuristic algorithm (MOGA-II). As the results show, considering products and returned parts in products, a simultaneous practice of forward and reverse supply chains leads to better product quality, less damage to the environment, and lower costs for customers.