Optimizing a fuzzy multi-objective closed-loop supply chain model considering financial resources using meta-heuristic.

This paper presents a multi-objective mathematical model to optimize and harmonize a supply chain in order to reduce costs, improve quality, and gain a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to enhance both quality and customer satisfaction and reduce the 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 first validated. The parameters of the proposed algorithm were then adjusted to solve the model using Multi-Objective Simulated Annealing (MOSA) algorithm. To validate the performance of the designed algorithm, some examples were solved based on General Algebraic Modeling System (GAMS). The MOSA algorithm achieved average errors of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in the average less than one minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS failed to reach an optimal solution for large problems in a reasonable computational time. The average error of the designed algorithm 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. [ABSTRACT FROM AUTHOR]