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Grasman, Scott Erwin

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https://academica-e.unavarra.es/handle/2454/49215

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Grasman

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Scott Erwin

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Estadística e Investigación Operativa

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7858

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2015 - 20162
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    PublicationOpen Access
    Electric vehicles in logistics and transportation: a survey on emerging environmental, strategic, and operational challenges
    (MDPI, 2016) Juan Pérez, Ángel Alejandro; Méndez, Carlos Alberto; Faulín Fajardo, Javier; Armas, Jesica de; Grasman, Scott Erwin; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
    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.
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    PublicationOpen Access
    A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems
    (Elsevier Ltd., 2015) Juan Pérez, Ángel Alejandro; Faulín Fajardo, Javier; Grasman, Scott Erwin; Rabe, Markus; Figueira, Gonçalo; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
    Many combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Metaheuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. After completing an extensive review of related work, this paper describes a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. ‘Simheuristics’ allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants) into a metaheuristic-driven framework. These optimization-driven algorithms rely on the fact that efficient metaheuristics already exist for the deterministic version of the corresponding COP. Simheuristics also facilitate the introduction of risk and/or reliability analysis criteria during the assessment of alternative high-quality solutions to stochastic COPs. Several examples of applications in different fields illustrate the potential of the proposed methodology.