Abstract
The purpose of this paper is to develop and test three meta-heuristic algorithms to solve large size multi-objective supply chain network design problems. The algorithms are based on tabu search, genetic algorithm, and simulated annealing to find near-optimal global solutions. The three algorithms are designed, coded, and tested, and their parameters are fine tuned. The exact ε-constraint algorithm embedded in the General Algebraic Modeling System is used to validate the results of the three algorithms. The algorithms are compared using a typical multi-objective supply chain model utilizing several performance measures. The measures include the mean ideal distance, diversification metric, and percent of domination, inverted generational distance, and computation time. The results show that the tabu search algorithm outperformed the other two algorithms in terms of the percent of domination and computation time. On the other hand, the simulated annealing solutions are the best in terms of their diversity. The work in this paper is expected to help managers to solve large-scale supply chain problems that arise in oil and gas, petrochemical, and food supply chains.
Original language | English |
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Pages (from-to) | 12223-12248 |
Number of pages | 26 |
Journal | Soft Computing |
Volume | 27 |
Issue number | 17 |
DOIs | |
State | Published - Sep 2023 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Keywords
- Genetic algorithm
- Meta-heuristic algorithms
- Multi-objective supply chain
- Simulated annealing
- Tabu search
ASJC Scopus subject areas
- Theoretical Computer Science
- Software
- Geometry and Topology