A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems
Fecha
2015Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Impacto
|
10.1016/j.orp.2015.03.001
Resumen
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 metaheuristi ...
[++]
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. [--]
Materias
Metaheuristics,
Simulation,
Combinatorial optimization,
Stochastic problems
Editor
Elsevier Ltd.
Publicado en
Operations Research Perpsectives, 2 (2015) 62-72
Departamento
Universidad Pública de Navarra. Departamento de Estadística e Investigación Operativa /
Nafarroako Unibertsitate Publikoa. Estatistika eta Ikerketa Operatiboa Saila
Versión del editor
Entidades Financiadoras
This work has been partially supported by the Spanish Ministry
of Economy and Competitiveness (grant TRA2013-48180-C3-P),
FEDER, and the Ibero-American Programme for Science and
Technology for Development (CYTED2014-515RT0489). Likewise
we want to acknowledge the support received by the Department
of Universities, Research & Information Society of the Catalan
Government (Grant 2014-CTP-00001) and the CAN Foundation
(Navarre, Spain) (Grant 3CAN2014-3758).