Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2025.069139
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

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 Chen1,*
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: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.069139

Received 16 June 2025; Accepted 21 November 2025; Published online 22 December 2025

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.

Keywords

Dynamic vehicle routing; multiple depots; ant colony optimization; temporal-spatial distance; time slice

  • 225

    View

  • 38

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link