Optimizing A Fuzzy Multi-Objective Closed-loop Supply Chain Model Considering Financial Resources using meta-heuristic

Document Type : Article


1 Qazvin Branch, Faculty of Industrial and Mechanical Engineering, Department of Industrial Engineering, Islamic Azad University, Qazvin, Iran

2 - Qazvin Branch, Faculty of Industrial and Mechanical Engineering, Department of Industrial Engineering, Islamic Azad University, Qazvin, Iran - School of Strategy and Leadership, Faculty of Business and Law, Coventry University, Coventry, United Kingdom

3 Bandar-e-Anzali International Islamic Azad Branch, Department of Industrial Engineering, Islamic Azad University, Bandar-e-Anzali, Guilan, Iran

4 - The Reliability, Risk, and Maintenance Research Laboratory (RRMR Lab), Mechanical and Industrial Engineering Department, Ryerson University, Toronto, Ontario, Canada - Distributed Systems & Multimedia Processing Laboratory (DSMP lab), Department of Computer Science, Ryerson University, Toronto, Ontario, Canada


This paper presents a multi-objective mathematical model which aims to optimize and harmonize a supply chain to reduce costs, improve quality, and achieve a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to increase quality and customer satisfaction and reduce production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was firstly validated. The algorithm's parameters are then adjusted to solve the model with the multi-objective simulated annealing (MOSA) algorithm. To validate the designed algorithm's performance, we solve some examples with General Algebraic Modeling System (GAMS). The MOSA algorithm has achieved an average error of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in average less than 1 minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS couldn't reach an optimal solution for the large problem in a reasonable computational time. The designed algorithm's average error was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper.


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