Open Access

ARTICLE

Solving Multi-Depot Vehicle Routing Problems with Dynamic Customer Demand Using a Scheduling System TS-DPU Based on TS-ACO

Tsu-Yang Wu¹, Chengyuan Yu¹, Yanan Zhao², Saru Kumari³, Chien-Ming Chen^1,*

1 School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China
3 Department of Mathematics, Chaudhary Charan Singh University, Meerut, 250004, Uttar Pradesh, India

* Corresponding Author: Chien-Ming Chen. Email:

Computers, Materials & Continua 2026, 86(3), 97 https://doi.org/10.32604/cmc.2025.069139

Received 16 June 2025; Accepted 21 November 2025; Issue published 12 January 2026

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.

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Keywords

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