@article{Wu2026, 
author = {Tsu-Yang Wu and Chengyuan Yu and Yanan Zhao and Saru Kumari and Chien-Ming Chen},
title = {Solving Multi-Depot Vehicle Routing Problems with Dynamic Customer Demand Using a Scheduling System TS-DPU Based on TS-ACO},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {3},
pages = {97},
keywords = {Dynamic vehicle routing, multiple depots, ant colony optimization, temporal-spatial distance, time slice},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.069139},
doi = {10.32604/cmc.2025.069139},
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.}
}