| DOI | Trouver le DOI : https://doi.org/10.1145/3557991.3567776 |
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| Auteur | Rechercher : Han, Jihee; Rechercher : Mozhdehi, Arash; Rechercher : Wang, Yunli1; Rechercher : Sun, Sun1; Rechercher : Wang, Xin |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems, Nov. 1, 2022, Seattle, Washington |
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| Sujet | vehicle routing problem; multi-trip; heterogeneous fleet; ant colony optimization |
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| Résumé | This paper deals with optimizing a practical variant of Vehicle Routing Problem (VRP), namely multi-trip VRP with heterogeneous fleet and time windows (MTVRPHFTW). To be able to solve this problem for industrial applications, we proposed an efficient constructive-based algorithm based on ant colony optimization (ACO) meta-heuristic. Two additional heuristics are proposed to further improve the performance of the algorithm. For evaluation, the proposed algorithm in this paper, named ACO algorithm with improvement mechanisms (IACO), is tested based on data provided by a logistics company in Canada with real-world settings. Experimental results of IACO demonstrates superiority of the proposed algorithm in terms of travelling cost, number of trips per vehicle, number of total trips, and balancing the load between the drivers compared to existing methods including the actual route history. |
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| Date de publication | 2022-11-03 |
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| Maison d’édition | ACM |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | db408661-095f-4307-89f7-e13b57b35ce6 |
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| Enregistrement créé | 2022-11-15 |
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| Enregistrement modifié | 2022-11-15 |
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