@Article{cmc.2025.069139,
AUTHOR = {Tsu-Yang Wu, Chengyuan Yu, Yanan Zhao, Saru Kumari, Chien-Ming Chen},
TITLE = {Solving Multi-Depot Vehicle Routing Problems with Dynamic Customer Demand Using a Scheduling System TS-DPU Based on TS-ACO},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v86n3/65412},
ISSN = {1546-2226},
ABSTRACT = {With the increasing complexity of logistics operations, traditional static vehicle routing models are no longer sufficient. In practice, customer demands often arise dynamically, and multi-depot systems are commonly used to improve efficiency. This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand. New customers appear in the delivery process at any time and are periodically optimized according to time slices. Then, we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem. The classical ant colony algorithm uses spatial distance to select nodes, while TS-ACO considers the impact of both temporal and spatial distance on node selection. Meanwhile, we adopt Cordeau’s Multi-Depot Vehicle Routing Problem with Time Windows (MDVRPTW) dataset to evaluate the performance of our system. According to the experimental results, TS-ACO, which considers spatial and temporal distance, is more effective than the classical ACO, which only considers spatial distance.},
DOI = {10.32604/cmc.2025.069139}
}