Design and Optimization of Inverse Logistic Network under Uncertainty Using Genetic Algorithm

Document Type : Original Article


1 Professor, Shahid Beheshti University.

2 Assistant Professor, K. N. Toosi University of Technology.

3 M.A., Islamic Azad University of Arak.


In the supply chain, return management is applied in reverse logistics. For various reasons, the flow of materials in the opposite direction of the chain is inevitable. It is essential to deal with the issue of reverse logistics network and efficient management and guidance. According to the reviews, one of the issues that has a great impact on reverse logistics network modeling is the uncertainty situation. In reverse logistics, parameters such as capacity of centers, demand, cost and quality, etc. are uncertain. In this regard, in this study, a possible mixed integer linear programming model for reverse logistics network design is presented. To solve this type of model, it must first become a definitive model. The model presented in this research is multi-product and multi-category that includes transportation costs and facility construction at the same time. The model is NP-Hard in terms of cost minimization (NP-Hard) in terms of cost minimization (facility deployment costs and shipping costs) and uncertainty in demand for return products. Exponentialism increases with respect to the dimensions of the problem. Therefore, in this study, an efficient method using Genetic Algorithm with priority-axis coding is proposed.


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