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Optimising Travel Routes for Tour Bus Operator
This project report delves into optimizing tour routes for bus operators serving diverse destinations using heuristics and algorithms. The primary aim is to enhance tourist transportation while optimizing tour bus routes through varied algorithms and heuristics. The study employs the Nearest Neighbour, Nearest Insertion, and Christofides algorithms, along with 2-opt enhancements, for comprehensive analysis and simulations. To further streamline route optimization, K-means clustering is strategically integrated. This approach groups nearby locations, enabling operators to visit a single representative site within each cluster. Passengers can conveniently access adjacent locations on foot from these cluster points. This technique significantly reduces computational complexity, especially when handling numerous locations, thus effectively reducing runtime. Through extensive experimentation involving clustered, dispersed, and varying destination scenarios, the study captures execution times and overall route durations, subjecting them to comparative assessment. This analysis showcases algorithm strengths and limitations under diverse conditions. In summary, this research advances tour route optimization and introduces efficient clustering strategies, offering operators a robust framework for informed decision-making. It empowers operators to create seamless, enjoyable travel experiences while minimizing computational burdens.
Journal/Conference/Book titleThe Ninth IRC Conference on Science, Engineering and Technology (IRC-SET 2023)