SIMULATION OF TRAVELING SALESMAN PROBLEM
Abstract
The Traveling Salesman Problem (TSP) is a classical optimization challenge in computer science and operations research that seeks the shortest route for a salesman to visit a set of cities exactly once and return to the starting point. Given its NP-hard complexity, exact solutions are computationally expensive for large datasets. This study aims to design and simulate TSP solutions using three algorithms-Brute Force, Nearest Neighbor, and Genetic Algorithm- to analyze their performance and scalability. Implemented in Python, the simulation uses synthetic datasets to evaluate route efficiency and visualize optimized paths. The project adopts a modular architecture that enhances flexibility and maintainability. Findings from this study serve both academic and practical purposes by offering insights into algorithm selection efficiency, with potential applications in logistics, supply chain optimization, and transportation planning.
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