e-Logistics: Sequential Dijkstra with Learning Bias for Efficient Truck Routing in Urban Areas

This research studies the optimization and the generation of the most optimized paths in heavy-traffic urban transport for trucks using modified and dynamic short path algorithms. Logisticians are always in need of effective methods to reduce the cost in the distribution process within a supply chain, and also to determine the best incoterm for their trades, either for sales or acquisition, thus the objective of this research is to equip logisticians with e-Logistics tools and technologies to help them adapt and adjust their planification to gain the most from the incoterm of their choice during every trade. Our focus is to maximize the benefits of the chosen incoterms for each transaction, as this study focuses specifically on urban areas with heavy traffic, which consistently present challenges for logistics optimization, particularly for trucks navigating designated international transport routes or urban delivery points.