Research Article
Cluster-Based Optimization Method for Delivery Networks
@INPROCEEDINGS{10.1007/978-3-031-55976-1_10, author={Cheng-Hui Chen and Yen-Shiuan Lin and Yung-Kuan Chan and Shyr-Shen Yu}, title={Cluster-Based Optimization Method for Delivery Networks}, proceedings={Smart Grid and Internet of Things. 7th EAI International Conference, SGIoT 2023, TaiChung, Taiwan, November 18-19, 2023, Proceedings}, proceedings_a={SGIOT}, year={2024}, month={3}, keywords={Route Optimization Delivery Networks Vehicle Allocation}, doi={10.1007/978-3-031-55976-1_10} }- Cheng-Hui Chen
Yen-Shiuan Lin
Yung-Kuan Chan
Shyr-Shen Yu
Year: 2024
Cluster-Based Optimization Method for Delivery Networks
SGIOT
Springer
DOI: 10.1007/978-3-031-55976-1_10
Abstract
Traditional logistics scheduling, which heavily relies on experienced personnel, can be time-consuming and prone to oversights, issues that are further amplified with the integration of new distributors. Addressing these challenges, this study proposes a unique cluster-based optimization method for delivery networks (COMDN). COMDN leverages extensive RFID signal data, incorporating delivery locations, spatial zones, and delivery priorities, among others. The process begins by collecting delivery locations and computing pairwise distances between distributors, followed by the clustering of suppliers based on these distances. The final stage involves constructing an optimal delivery route, assisting in distribution to diverse, dispersed, and complex locations on the map, thereby ensuring a balanced delivery to each location and establishing shorter delivery paths. This results in a significant reduction in order processing times. Using data from a prominent tobacco and alcohol distributor in central Taiwan, the study implements shipment scheduling and route optimization. Experimental results reveal that COMDN, when compared to previous manual methods, shows a significant 2.98% improvement over existing procedures, demonstrating its efficiency and applicability in a wide range of multi-objective delivery and logistics scenarios.
