Optimization of Route Planning and Clustering Using Road Network Distances: The Case of RouteIQ

Due to the rapid expansion of new e-commerce trends, complex delivery networks and unparalleled consumer demands, logistics and supply chain operations are more sophisticated than ever before–making route optimization a require advancement. Such integrate real-world road network data and dynamic clustering method in route planning/clustering They introduce a system RouteIQ, designed to optimize planning. The RouteIQ engine does sophisticated real-time distance or road calculations using the Open Source Routing Machine (OSRM) to achieve dynamic route optimization. We implemented techniques such as the Traveling Salesman Problem (TSP) and AI-based clustering to improve delivery clusters, thus also addressing the variable traffic situations, closures of roads, or normal fluctuations in deliveries needed. Results show the model achieves operational efficiency improvements with 15 Percent less travel distance and optimal delivery times by 20 Percent. With the help of dynamic threshold-based clustering , cluster size and travel distance can be customized for balanced workload distribution and improved resource utilization. We used visualization tools (interactive maps and OSM-based heat maps) to generate actionable delivery patterns 1 including quick evaluations of clustering appropriate deliveries for effective assessment of clusters. The research notes the capability of RouteIQ to provide scalable and customization solutions for logistics optimization making delivery cost ineffective , organized with minimum manpower. Real-time traffic data integration to this algorithm, scalability to large crowds through cloud based solution and pre-dictive analytics for pro-active remedy measures are few of the future directions around which we could potentially build continued growth in adaptability with changing logistics needs.