A new optimization model for closed-loop supply chain networks

Abstract

Increasing environmental concerns require businesses to become more responsive to products that either have been returned or that are at the end of their useful lives. Organization's responsiveness and their reactions toward life cycles of products are critical to achieve sustained success once fluctuations are recurrent and the business environments are turbulent. Life cycles are getting shorter, and efficient handling can save large amounts of cash since many materials can be extracted, reused, and redistributed. Reverse Logistics (RL) and Closed Loop Supply Chains (CLSC) have garnered growing interest as a way to manage this reverse flow of products in a cost effective way. It is discernible that the degree of complexity in closed loop networks is usually higher than open networks. In a closed loop supply chain network, the attempt is to ensure a smooth flow of materials as well as extracting the maximum value from returning and end-of-life goods. Reduction of waste and generating profit for enterprises are two paramount achievements through integrating forward and reverse logistics. By considering various proposed conceptual models for CLSC, in this study at first we present a comprehensive applicable conceptual model. Then, we specifically focus on a CLSC network, which includes multiple plants, collection centers, demand markets, and products. To this aim, a generalized proposed mixed-integer linear programming model is presented that minimizes the total cost. The objective is to know how many, which production plants and Return Processors (Collection sites) should be open, which products, and in which quantities should have stuck. The goal of this model is to minimize the supply chain waste and reduce supply chain costs. In such a way, the supply chain system can be agile, integrated, robust, and lean. The proposed model can be extended to consider more environmental factors. Moreover, the efficacy and efficiency of that have been validated by case study and numerical example.