Solving the Transportation Problem using Meta-Heuristic Algorithms

Document Type : Research Article

Authors

1 Department of Mathematics and Computer Science, Faculty of Sciences, University of Zanjan, Zanjan, Iran

2 School of Mathematics and Computer Sciences, Damghan University, P.O. Box 36715-364, Damghan, Iran

Abstract

In this paper, the transportation problem is thoroughly analyzed and solved using three different meta-heuristic algorithms. The transportation problem, a fundamental optimization issue in operations research, involves determining the most efficient way to distribute goods from multiple supply sources to multiple destinations while minimizing overall transportation costs. Traditional exact methods may struggle to provide solutions in a reasonable time frame, especially as the size and complexity of the problem grow. In contrast, meta-heuristic algorithms offer the potential to find near-optimal solutions more efficiently, making them a valuable approach for large-scale problems. This study focuses on three algorithms: Genetic Algorithm (GA), Teaching-Learning-Based Optimization (TLBO), and an improved variant of TLBO known as ITLBO. Each of these algorithms was applied to the transportation problem, and their performance was evaluated in terms of solution quality, convergence speed, and computational efficiency. The results demonstrate that while all three algorithms can solve the transportation problem, ITLBO consistently outperforms GA and TLBO {in terms of accuracy}. Specifically, ITLBO shows a faster convergence to the optimal solution and a significant reduction in execution time, particularly for large problem instances. The improved efficiency of ITLBO makes it a more practical and scalable option for solving complex transportation problems. 

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Analytical and Numerical Solutions for Nonlinear Equations
Volume 9, Issue 1
May 2024
Pages 12-19

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History

  • Receive Date: 15 February 2025
  • Revise Date: 24 March 2025
  • Accept Date: 06 April 2025
  • Publish Date: 20 April 2025

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