Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges

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Juan, Angel Alejandro ; Mendez, Carlos Alberto ; Faulin, Javier ; de Armas, Jesica ; Grasman, Scott (2017)
  • Publisher: MDPI
  • Journal: (issn: 1996-1073)
  • Related identifiers: doi: 10.3390/en9020086, doi: 10.3390/en9070546
  • Subject: Routing | Ingeniería de Sistemas y Comunicaciones | logistics and transportation | electric vehicles | T | Green Logistics | INGENIERÍAS Y TECNOLOGÍAS | Technology | Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información | green vehicle routing problems

Current logistics and transportation (L&T) systems include heterogeneous fleets consisting of common internal combustion engine vehicles as well as other types of vehicles using ?green? technologies, e.g., plug-in hybrid electric vehicles and electric vehicles (EVs). However, the incorporation of EVs in L&T activities also raise some additional challenges from the strategic, planning, and operational perspectives. For instance, smart cities are required to provide recharge stations for electric-based vehicles, meaning that investment decisions need to be made about the number, location, and capacity of these stations. Similarly, the limited driving-range capabilities of EVs, which are restricted by the amount of electricity stored in their batteries, impose non-trivial additional constraints when designing efficient distribution routes. Accordingly, this paper identifies and reviews several open research challenges related to the introduction of EVs in L&T activities, including: (a) environmental-related issues; and (b) strategic, planning and operational issues associated with ?standard? EVs and with hydrogen-based EVs. The paper also analyzes how the introduction of EVs in L&T systems generates new variants of the well-known Vehicle Routing Problem, one of the most studied optimization problems in the L&T field, and proposes the use of metaheuristics and simheuristics as the most efficient way to deal with these complex optimization problems. Fil: Juan, Angel Alejandro. Universitat Oberta de Catalunya; España Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Faulin, Javier. Universidad de Navarra; España Fil: de Armas, Jesica. Universitat Oberta de Catalunya; España Fil: Grasman, Scott. Rochester Institute of Technology; Estados Unidos