Multi-Objective Electric Vehicle Route and Charging Planning with Contraction Hierarchies

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00372690" target="_blank" >RIV/68407700:21230/24:00372690 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1609/icaps.v34i1.31467" target="_blank" >https://doi.org/10.1609/icaps.v34i1.31467</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1609/icaps.v34i1.31467" target="_blank" >10.1609/icaps.v34i1.31467</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Objective Electric Vehicle Route and Charging Planning with Contraction Hierarchies

Popis výsledku v původním jazyce

Electric vehicle (EV) travel planning is a complex task that involves planning the routes and the charging sessions for EVs while optimizing travel duration and cost. We show the applicability of the multi-objective EV travel planning algo- 5 rithm with practically usable solution times on country-sized road graphs with a large number of charging stations and a realistic EV model. The approach is based on multi-objective A* search enhanced by Contraction hierarchies, optimal dimensionality reduction, and sub-optimal ϵ-relaxation tech10 niques. We performed an extensive empirical evaluation on 182 000 problem instances showing the impact of various algorithm settings on real-world map of Bavaria and Germany with more than 12 000 charging stations. The results show the proposed approach is the first one capable of performing 15 such a genuine multi-objective optimization on realistically large country-scale problem instances that can achieve practically usable planning times in order of seconds with only a minor loss of solution quality. The achieved speed-up varies from ~ 11x for optimal solution to more than 250x for sub20 optimal solution compared to vanilla multi-objective A*.

Název v anglickém jazyce

Multi-Objective Electric Vehicle Route and Charging Planning with Contraction Hierarchies

Popis výsledku anglicky

Electric vehicle (EV) travel planning is a complex task that involves planning the routes and the charging sessions for EVs while optimizing travel duration and cost. We show the applicability of the multi-objective EV travel planning algo- 5 rithm with practically usable solution times on country-sized road graphs with a large number of charging stations and a realistic EV model. The approach is based on multi-objective A* search enhanced by Contraction hierarchies, optimal dimensionality reduction, and sub-optimal ϵ-relaxation tech10 niques. We performed an extensive empirical evaluation on 182 000 problem instances showing the impact of various algorithm settings on real-world map of Bavaria and Germany with more than 12 000 charging stations. The results show the proposed approach is the first one capable of performing 15 such a genuine multi-objective optimization on realistically large country-scale problem instances that can achieve practically usable planning times in order of seconds with only a minor loss of solution quality. The achieved speed-up varies from ~ 11x for optimal solution to more than 250x for sub20 optimal solution compared to vanilla multi-objective A*.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling

ISBN

978-1-57735-889-3

ISSN

2334-0835

e-ISSN

2334-0843

Počet stran výsledku

9

Strana od-do

114-122

Název nakladatele

AAAI Press

Místo vydání

Menlo Park, California

Místo konání akce

Banff

Datum konání akce

1. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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