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Research paper awarded "Energies Best Paper"

Research paper awarded "Energies Best Paper"

28.03.2019

 

A paper authored by Jésica de Armas has been selected for the Research Paper prize of "2016 Energies Best Paper Award"

The open access journal Energies (JCR indexed) has awarded the following paper with the prize "2016 Energies Best Paper Award".

Angel A Juan, Carlos A Mendez, Javier Faulin, Jesica de Armas and Scott E. Grasman (2016). Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges. Energies, 9(2), 86; https://doi.org/10.3390/en9020086

This prize brings prestige and relevance in the field, as well as an economic endowment.

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. View Full-Text

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