solving the transportation problem using meta-heuristic algorithms

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.