Faulín Fajardo, Javier
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Faulín Fajardo
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Javier
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Estadística, Informática y Matemáticas
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ISC. Institute of Smart Cities
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Publication Open Access Horizontal collaboration in freight transport: concepts, benefits and environmental challenges(Institut d'Estadística de Catalunya, 2017) Serrano Hernández, Adrián; Juan Pérez, Ángel Alejandro; Faulín Fajardo, Javier; Pérez Bernabeu, Elena; Estatistika eta Ikerketa Operatiboa; Institute of Smart Cities - ISC; Estadística e Investigación OperativaSince its appearance in the 1990s, horizontal collaboration (HC) practices have revealed them-selves as catalyzers for optimizing the distribution of goods in freight transport logistics. After introducing the main concepts related to HC, this paper offers a literature review on the topic and provides a classification of best practices in HC. Then, the paper analyses the main benefits and optimization challenges associated with the use of HC at the strategic, tactical, and operational levels. Emerging trends such as the concept of ‘green’ or environmentally-friendly HC in freighttransport logistics are also introduced. Finally, the paper discusses the need of using hybrid optimization methods, such as simheuristics and learnheuristics, in solving some of the previously identified challenges in real-life scenarios dominated by uncertainty and dynamic conditions.Publication Open 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 OperatiboaMany 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.