A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems

Detalhes bibliográficos
Autor(a) principal: Juan,AA
Data de Publicação: 2015
Outros Autores: Faulin,J, Grasman,SE, Rabe,M, Gonçalo Reis Figueira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/5684
http://dx.doi.org/10.1016/j.orp.2015.03.001
Resumo: 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. Meta-heuristics 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. (c) 2015 The Authors. Published by Elsevier Ltd.
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spelling A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problemsMany 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. Meta-heuristics 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. (c) 2015 The Authors. Published by Elsevier Ltd.2018-01-08T09:13:46Z2015-01-01T00:00:00Z2015info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5684http://dx.doi.org/10.1016/j.orp.2015.03.001engJuan,AAFaulin,JGrasman,SERabe,MGonçalo Reis Figueirainfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:33Zoai:repositorio.inesctec.pt:123456789/5684Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:18.271658Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems
title A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems
spellingShingle A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems
Juan,AA
title_short A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems
title_full A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems
title_fullStr A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems
title_full_unstemmed A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems
title_sort A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems
author Juan,AA
author_facet Juan,AA
Faulin,J
Grasman,SE
Rabe,M
Gonçalo Reis Figueira
author_role author
author2 Faulin,J
Grasman,SE
Rabe,M
Gonçalo Reis Figueira
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Juan,AA
Faulin,J
Grasman,SE
Rabe,M
Gonçalo Reis Figueira
description 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. Meta-heuristics 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. (c) 2015 The Authors. Published by Elsevier Ltd.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01T00:00:00Z
2015
2018-01-08T09:13:46Z
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http://dx.doi.org/10.1016/j.orp.2015.03.001
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