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Eskandari, Zahra, Avakh Darestani, Soroush, Imannezhad, Rana and Sharifi, Mani (2021) Optimizing a fuzzy multi-objective closed-loop supply chain model considering financial resources using meta-heuristic. Scientia Iranica. pp. 1-44. ISSN 2345-3605 (In Press)
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Iranica Scientia paper Sep 2021.pdf - Accepted Version Download (1MB) | Preview |
Abstract / Description
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
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | supply chain; metaheuristics; logistics; fuzzy sets; multi-objective |
| Subjects: | 500 Natural Sciences and Mathematics > 510 Mathematics 600 Technology > 650 Management & auxiliary services |
| Department: | Guildhall School of Business and Law |
| Depositing User: | Soroush Avakh Darestani |
| Date Deposited: | 27 Sep 2021 08:53 |
| Last Modified: | 27 Sep 2021 08:53 |
| URI: | https://repository.londonmet.ac.uk/id/eprint/6994 |
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