Routing Optimization for The Traveling Salesman Problem with Drones (TSP-D) in Medical Supply Distribution
DOI:
https://doi.org/10.23055/ijietap.2026.33.5.11587Keywords:
Traveling Salesman Problem with Drone (TSP-D), Drone, K-means, Medical Supply DistributionAbstract
The integration of drones into last-mile logistics has emerged as a transformative solution to address the inefficiencies of traditional truck-based delivery in smart cities. This study investigates the Traveling Salesman Problem with Drones (TSP-D) specifically within the context of medical supply distribution in Qingdao, China. A hierarchical two-stage optimization methodology is proposed: first, the service area is partitioned into optimal subregions using the k-means clustering algorithm, with the number of clusters determined by the silhouette coefficient. Second, a mixed-integer programming model is formulated and solved using Gurobi to optimize synchronized routing within each cluster. Based on a dataset of 169 customer locations, computational experiments demonstrate that the framework identifies seven efficient clusters and significantly enhances delivery performance. The findings indicate that the proposed framework has the potential to reduce response time and improve operational efficiency in medical supply distribution.
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