Performance Evaluation of Meta-Heuristic Algorithms for Designing Multi-Objective Multi-Product Multi-Echelon Supply Chain Network

Abstract

Abstract In recent years, interest in designing multi-echelon, multi-product supply chains using multi-objective optimization has surged. This growing interest is exemplified by the number of studies published in this field. The resulting models for these cases are complex multi-objective optimization network models of a combinatorial nature. Exact algorithms can at best provide an Pareto optimal solution for medium size problems. In such situations, metaheuristic algorithms become a viable option for solving these kinds of problems. Therefore, the purpose of this paper is to develop 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, tested, and their parameters are fine tuned. The exact ε-constraint algorithm embedded in the General Algebraic Modeling System (GAMS) is used to validate the results of the three algorithms. A well-designed study is used to compare the performance of the three algorithms based on several performance measures using sound statistical tests. A typical multi-objective supply chain model is used to compare the algorithms’ performance. 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.