Electric freight vehicles (EVs) are a sustainable alternative to conventional internal combustion freight vehicles. The driving autonomy of EVs is a fundamental component in the planning of EV routes for goods distribution. In this respect, a complicating factor lies in the fact that EVs’ energy consumption is subject to a great deal of uncertainty, which is due to a number of endogenous and exogenous factors. Ignoring such uncertainties in the planning of EV routes may lead a vehicle to run out of energy, which -given the scarcity of recharging stations- may have dire effects. Thus, to foster a widespread use of EVs, we need to adopt new routing strategies that explicitly account for energy consumption uncertainty. In this paper, we propose a new two-stage stochastic programming formulation for the single electric vehicle routing problem with stochastic energy consumption. Furthermore, we develop a decomposition algorithm for this problem. We provide an illustrative example showing the added value of incorporating uncertainty in the route planning process. We perform a variety of computational experiments and show that our decomposition algorithm is capable of efficiently solving instances with 20 customers and 30 scenarios.


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    Titel :

    The Electric Vehicle Route Planning Problem with Energy Consumption Uncertainty


    Beteiligte:
    Bruni, M.E. (Autor:in) / Jabali, O (Autor:in) / Khodaparasti, S (Autor:in)


    Erscheinungsdatum :

    03.11.2020


    Format / Umfang :

    179226 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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