Logistics Hub Location Optimization: A K-Means and P-Median Model Hybrid
  Approach Using Road Network Distances
Muhammad Abdul Rahman Muhammad Aamir Basheer Zubair Khalid Muhammad Tahir Momin Uppal
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.