Delivery Zones Partitioning Considering Workload Balance Using Clustering Algorithm

Abstract

This research proposes a novel approach for partitioning delivery zones in Bangkok that utilizes a combination of clustering and iterative algorithms. The approach leverages 30 days of delivery data to create delivery zones that having balanced workloads for drivers. The study begins by analyzing the delivery data to confirm the presence of unbalanced workloads across drivers within the 30-day period. To solve this imbalance, we use iterative k-means to adjust delivery zones considering the number of deliveries within the zone. The effectiveness of the approach was evaluated using two sets of parameters: geographic coordinates (latitude and longitude) and actual travel distance to reflect real-world scenarios. Regardless of the parameter set used, the experiments yielded balanced transportation areas with evenly distributed workloads. This approach demonstrates an improvement in workload equality compared to the original workload distribution.