Evaluation of Last-Mile Delivery Depot Locations for Third-Party Logistics Companies Using the K-Means Algorithm
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The study assesses the efficiency of last-mile delivery (LMD) depot locations for third-party logistics companies in Pontianak, West Kalimantan, using the K-means clustering algorithm. The goal is to optimize new depot placements to reduce delivery distances and operational costs. Data were gathered via field observations and Google Maps to map depots, customers, and geographic features. K-means clustered customer locations, and depot efficiency was measured by Euclidean distance to cluster centroids—shorter distances indicating better alignment.The results show minor distance deviations among some companies, but no significant efficiency differences were found, suggesting that depot strategies remain suboptimal and could benefit from more data-driven optimization. This study offers practical insights on using K-means for depot placement and underscores the need to refine clustering methods for better LMD performance.
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