Delivery Zones Partitioning Considering Workload Balance Using Clustering Algorithm
Issued Date
2024-01-01
Resource Type
Scopus ID
2-s2.0-85203067785
Journal Title
Proceedings of the 19th International Conference on Software Technologies, ICSOFT 2024
Start Page
378
End Page
385
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SCOPUS
Bibliographic Citation
Proceedings of the 19th International Conference on Software Technologies, ICSOFT 2024 (2024) , 378-385
Suggested Citation
Wangwattanakool J., Laesanklang W. Delivery Zones Partitioning Considering Workload Balance Using Clustering Algorithm. Proceedings of the 19th International Conference on Software Technologies, ICSOFT 2024 (2024) , 378-385. 385. doi:10.5220/0012803800003758 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/101166
Title
Delivery Zones Partitioning Considering Workload Balance Using Clustering Algorithm
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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.