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Clustering-based enhanced ant colony optimization for multi-trip vehicle routing problem with heterogeneous fleet and time windows: an industrial case study

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DOITrouver le DOI : https://doi.org/10.1145/3681772.3698216
AuteurRechercher : Identifiant ORCID : https://orcid.org/0009-0004-6419-1544; Rechercher : Identifiant ORCID : https://orcid.org/0000-0002-9938-3560; Rechercher : 1Identifiant ORCID : https://orcid.org/0000-0002-2320-954X; Rechercher : 1Identifiant ORCID : https://orcid.org/0000-0001-7870-9448; Rechercher : Identifiant ORCID : https://orcid.org/0000-0003-3569-2126
Affiliation
  1. Conseil national de recherches Canada. Technologies numériques
Bailleur de fondsRechercher : National Research Council Canada
FormatTexte, Article
ConférenceSIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems, October 29 - November 1, 2024, Atlanta, Georgia, United States
Sujetvehicle routing problem; workload balance; ant colony optimization; DBSCAN+; micro-clustering; route optimization
Résumé
This paper introduces a novel approach to solving a practical variant of the Vehicle Routing Problem (VRP), the multi-trip VRP with heterogeneous fleet and time windows (MTVRPHFTW). The approach integrates an improved Ant Colony Optimization (IACO) metaheuristic, a modified density-based spatial clustering of application with noise (DBSCAN-Plus) clustering, and a Micro-Cluster Fusion Scheme. The proposed framework aims to optimize vehicle routes by minimizing total travelling distance and time while ensuring a fair distribution of workload among the vehicles (drivers). To evaluate the proposed algorithm, referred to as the Ant Colony Optimization (ACO) algorithm with improvement mechanisms (Cluster Improved Ant Colony Optimization, CIACO), real-world data from a logistics company in Canada was utilized. This empirical testing aims to validate the algorithm’s effectiveness in practical applications. The experimental results of CIACO demonstrate that the proposed algorithm outperforms existing methods in terms of reducing traveling distance, minimizing traveling time, optimizing the use of smaller vehicles to reduce CO2 emissions, achieving balanced workloads among drivers, and improving overall route optimization.
Date de publication
Maison d’éditionACM
Dans
  • Proceedings of the 17th ACM SIGSPATIAL International Workshop on Computational Transportation Science GenAI and Smart Mobility Session (28 janvier 2025) : 46–55. .
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement6c1a803a-ac77-4602-aa90-9cbe3890b939
Enregistrement créé2025-02-03
Enregistrement modifié2025-11-03

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