2023/08/18 by Muhammad Abdul Rahman, Muhammad Aamir Basheer, Rahman, Muhammad Abdul +7
Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Maritime Ports and Logistics #Optimization and Control (math.OC) #Urban and Freight Transport Logistics #Vehicle Routing Optimization Methods
paper · pdf · doi:10.48550/arxiv.2308.11038
openalex publication_date 2023/08/18 · openalex created_date 2023/08/24 · openalex updated_date 2026/07/28
Logistic hubs play a pivotal role in the last-mile delivery distance; even a slight increment in distance negatively impacts the business of the e-commerce industry while also increasing its carbon footprint. The growth of this industry, particularly after Covid-19, has further intensified the need for optimized allocation of resources in an urban environment. In this study, we use a hybrid approach to optimize the placement of logistic hubs. The approach sequentially employs different techniques. Initially, delivery points are clustered using K-Means in relation to their spatial locations. The clustering method utilizes road network distances as opposed to Euclidean distances. Non-road network-based approaches have been avoided since they lead to erroneous and misleading results. Finally, hubs are located using the P-Median method. The P-Median method also incorporates the number of deliveries and population as weights. Real-world delivery data from Muller and Phipps (M&P) is used to demonstrate the effectiveness of the approach. Serving deliveries from the optimal hub locations results in the saving of 815 (10%) meters per delivery.