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http://hdl.handle.net/10609/92802
Title: A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems
Author: Juan Pérez, Ángel Alejandro
Faulin Fajardo, Francisco Javier
Grasman, Scott Erwin
Rabe, Markus
Figueira, Gonçalo
Others: Universidad Pública de Navarra
Universidade do Porto
University of Dortmund
Rochester Institute of Technology
Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Keywords: metaheuristics
simulation
combinatorial optimization
stochastic problems
Issue Date: 1-Dec-2015
Publisher: Operations Research Perspectives
Citation: Juan, A.A., Faulín, F., Grasman, S., Rabe, M. & Figueira, G. (2015). A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2(), 62-72. doi: 10.1016/j.orp.2015.03.001
Project identifier: info:eu-repo/grantAgreement/TRA2013-48180-C3-P
info:eu-repo/grantAgreement/CYTED2014-515RT0489
info:eu-repo/grantAgreement/2014-CTP-00001
info:eu-repo/grantAgreement/3CAN2014-3758
Also see: https://doi.org/10.1016/j.orp.2015.03.001
Abstract: 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.
Language: English
URI: http://hdl.handle.net/10609/92802
ISSN: 2214-7160MIAR
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