Highlights Electric vehicle sharing system is jointly optimized for both infrastructure planning and fleet operation decisions. A multi-stage stochastic model is developed to consider dynamic uncertain demand. Effective solution methods are developed based on Lagrangian relaxation and stochastic dual dynamic programming. Managerial insights are developed through a series of case studies.

    Abstract We propose an innovative framework to deploy a one-way Electric Vehicle (EV) sharing system that serves an urban area. For the first time, long-term infrastructure planning (charging station location and fleet distribution) and real-time fleet operations (relocation and charging decisions) are jointly optimized under time-varying uncertain demand. This substantially advances EV sharing system efficiency and yields a practical management strategy. We propose a multistage stochastic model to address the critical challenge of time-varying uncertain demand. An accelerated solution algorithm is developed to conquer the curse of dimensionality in integer infrastructure planning decisions. Meaningful insights are delivered through hypothetical numerical experiments and a realistic case study with EV sharing service in the New York City.


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    Titel :

    Joint infrastructure planning and fleet management for one-way electric car sharing under time-varying uncertain demand


    Beteiligte:
    Hua, Yikang (Autor:in) / Zhao, Dongfang (Autor:in) / Wang, Xin (Autor:in) / Li, Xiaopeng (Autor:in)


    Erscheinungsdatum :

    2019-07-13


    Format / Umfang :

    22 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch