Developing a Multi-Objective Model for Locating-Routing-Inventory Problem in a Multi-Period and Multi-Product Green Closed-Loop Supply Chain Network for Perishable Products

Document Type : Research Paper


1 Ph.D. Candidate, Department of Industrial Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Associate Prof., Department of Industrial Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Prof., Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

4 Prof., Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran


Objective: During the last three decades, the concept of decision-making integrity in the supply chain has become one of the most important dimensions in the supply chain management. This concept examines the dependence between the location of facilities, allocation of flow between facilities, the structure of transport system and inventory control system. This paper presents a new form of locating- routing- inventory problem in a closed-loop supply chain network for perishable products with respect to environmental considerations, in such a way that the aggregate system costs, the aggregate maximum transportation time and emission of pollutants throughout the network should be minimized.
Methods: The research problem is formulated in the form of a multi-objective mixed integer nonlinear programming model and a genetic algorithm approach is proposed to solve the model. In order to validate, the results of the proposed algorithm are compared in small-scale examples with the exact solution method using GAMS software.
Results: The mean error of the proposed algorithm for the objective function is fewer than 4% as compared to the exact solution. In addition, the results of the algorithm's performance are discussed based on standard indices. The computational results indicated the efficiency of the algorithm for a wide range of problems with different sizes.
Conclusion: The locating, routing and inventory decisions are interdependent and determining the optimal values for these variables is in interaction with each other which can lead to an optimal system with the least possible cost.


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