Abstract
This study proposes a hybrid approach to route optimization, comparing and combining Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Dynamic Programming (DP) to solve the Traveling Salesman Problem (TSP) and similar routing challenges. The objective is to minimize travel time and cost by incorporating real-time road data from OpenRouteService and Google Maps APIs. The hybrid algorithms are tested on large datasets, demonstrating their scalability and adaptability to real-world complexities such as fluctuating traffic conditions. Through mathematical modelling and pseudocode, the performance of each algorithm is compared, highlighting their effectiveness in optimizing logistics operations. The results indicate that this approach significantly reduces computational time and operational costs, providing a robust solution for modern logistics.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
