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Pregled bibliografske jedinice broj: 1260343

A multi-agent system for context-aware electric vehicle fleet routing: A step towards more sustainable urban operations


Jelen, Goran; Babic, Jurica; Podobnik, Vedran
A multi-agent system for context-aware electric vehicle fleet routing: A step towards more sustainable urban operations // Journal of cleaner production, 374 (2022), 134047, 18 doi:10.1016/j.jclepro.2022.134047 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1260343 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
A multi-agent system for context-aware electric vehicle fleet routing: A step towards more sustainable urban operations

Autori
Jelen, Goran ; Babic, Jurica ; Podobnik, Vedran

Izvornik
Journal of cleaner production (0959-6526) 374 (2022); 134047, 18

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Electric vehicle routing problem ; Multi-agent system ; Contextually enriched data ; Data science ; Sustainable urban operations

Sažetak
This paper presents a multi-agent system for context-aware routing of electric vehicle fleets, which provides a means for cost-effective planning and utilization of resources in contemporary urban operations. The multiagent system consists of three main parts: Model, Routing Algorithms and Platform. The case study of cleaning urban areas in the city of Split with electric cleaners is used to evaluate the multi-agent system. The model consists of three main entities: Electric Vehicle, Charging Station and Depot. The electric vehicle is defined more generally to ensure the application and reusability of the model in different business and research domains. The routing algorithms of the multi-agent system are defined with artificial intelligence utilization models. The utilization models predict the use of parking lots and charging stations, based on which the routing algorithms navigate the electric vehicles in the space. The utilization models use the CatBoost machine learning method and a contextually enriched dataset that uses point-of- interest data as context. The multi-agent platform for contextual routing of electric vehicles is used for validation and evaluation of multi-agent models and comparative analysis of routing algorithms under defined contextual conditions. Using context-aware routing algorithms, the multi- agent system for the case study showed a 5.6% improvement in urban cleaning operations and an 18.5% improvement in vehicle charging at charging stations.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Tehnologija prometa i transport



POVEZANOST RADA


Profili:

Avatar Url Jurica Babić (autor)

Avatar Url Vedran Podobnik (autor)

Avatar Url Goran Jelen (autor)

Poveznice na cjeloviti tekst rada:

doi www.sciencedirect.com

Citiraj ovu publikaciju:

Jelen, Goran; Babic, Jurica; Podobnik, Vedran
A multi-agent system for context-aware electric vehicle fleet routing: A step towards more sustainable urban operations // Journal of cleaner production, 374 (2022), 134047, 18 doi:10.1016/j.jclepro.2022.134047 (međunarodna recenzija, članak, znanstveni)
Jelen, G., Babic, J. & Podobnik, V. (2022) A multi-agent system for context-aware electric vehicle fleet routing: A step towards more sustainable urban operations. Journal of cleaner production, 374, 134047, 18 doi:10.1016/j.jclepro.2022.134047.
@article{article, author = {Jelen, Goran and Babic, Jurica and Podobnik, Vedran}, year = {2022}, pages = {18}, DOI = {10.1016/j.jclepro.2022.134047}, chapter = {134047}, keywords = {Electric vehicle routing problem, Multi-agent system, Contextually enriched data, Data science, Sustainable urban operations}, journal = {Journal of cleaner production}, doi = {10.1016/j.jclepro.2022.134047}, volume = {374}, issn = {0959-6526}, title = {A multi-agent system for context-aware electric vehicle fleet routing: A step towards more sustainable urban operations}, keyword = {Electric vehicle routing problem, Multi-agent system, Contextually enriched data, Data science, Sustainable urban operations}, chapternumber = {134047} }
@article{article, author = {Jelen, Goran and Babic, Jurica and Podobnik, Vedran}, year = {2022}, pages = {18}, DOI = {10.1016/j.jclepro.2022.134047}, chapter = {134047}, keywords = {Electric vehicle routing problem, Multi-agent system, Contextually enriched data, Data science, Sustainable urban operations}, journal = {Journal of cleaner production}, doi = {10.1016/j.jclepro.2022.134047}, volume = {374}, issn = {0959-6526}, title = {A multi-agent system for context-aware electric vehicle fleet routing: A step towards more sustainable urban operations}, keyword = {Electric vehicle routing problem, Multi-agent system, Contextually enriched data, Data science, Sustainable urban operations}, chapternumber = {134047} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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