Global warming caused by high carbon dioxide emissions is a critical issue. Transportation systems are a major contributor especially commercial vehicle fleets which typically use highly polluting diesel. Internal combustion engine vehicles (ICEVs) also emit particulates which are hazardous to health. Consequently, there is a drive to adopt electric vehicles (EVs) as greener and cheaper transport. For commercial vehicle fleets transitioning to EVs is problematic due to limited range and capacity factors. However, optimisation of EV routes can mitigate these issues. This paper explores using evolutionary optimisation to route EVs for a real-world delivery company. Moreover, the method is used to explore the types and numbers of EVs that provide best performance when minimising distance, costs and emissions. Experiments demonstrate using EVs to minimise cost and emissions is not straightforward. In fact, a mix of EVs and ICEVs provides best cost savings with reduced emissions.


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    Titel :

    Using Evolutionary Routing Optimisation to Transition to Electric Vehicle Fleets


    Weitere Titelangaben:

    Advs in Intelligent Syst., Computing


    Beteiligte:
    Panoutsos, George (Herausgeber:in) / Mahfouf, Mahdi (Herausgeber:in) / Mihaylova, Lyudmila S (Herausgeber:in) / Chitty, Darren M. (Autor:in) / Yates, William B. (Autor:in) / Keedwell, Ed (Autor:in)

    Kongress:

    UK Workshop on Computational Intelligence ; 2022 ; Sheffield, United Kingdom September 07, 2022 - September 09, 2022



    Erscheinungsdatum :

    19.05.2024


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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