Robust covering problems: formulations, algorithms and application

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dc.creatorAmadeu Almeida Coco
dc.date.accessioned2019-08-10T00:29:42Z
dc.date.accessioned2025-09-09T01:00:27Z
dc.date.available2019-08-10T00:29:42Z
dc.date.issued2017-10-06
dc.identifier.urihttps://hdl.handle.net/1843/ESBF-AYUMW2
dc.languagePortuguês
dc.publisherUniversidade Federal de Minas Gerais
dc.rightsAcesso Aberto
dc.subjectAlgorítmos
dc.subjectLogística
dc.subjectOtimização combinatória
dc.subjectPesquisa operacional
dc.subjectComputação
dc.subjectMeta-heurísticas
dc.subject.otherAlgoritmos
dc.subject.otherLogística
dc.subject.otherMeta-heurísticas
dc.subject.otherPesquisa Operacional
dc.subject.otherOtimização Combinatória
dc.titleRobust covering problems: formulations, algorithms and application
dc.typeTese de doutorado
local.contributor.advisor-co1Andrea Cynthia Santos
local.contributor.advisor1Thiago Ferreira de Noronha
local.contributor.referee1Andrea Cynthia Santos
local.contributor.referee1Sebastián Alberto Urrutia
local.contributor.referee1Christophe Duhamel
local.contributor.referee1Philippe Yves Paul Michelon
local.description.resumoTwo robust optimization NP-Hard problems are studied in this thesis: the min-max regret WSCP and the min-max regret MCLP. The uncertain data in these problems is modeled by intervals and only the minimum and maximum values for each interval are known. While the min-max regret WSCP is still a theoretical problem, the min-max regret MCLP has an application in disaster logistics which is investigated in this thesis. Four mathematical formulations, three exact algorithms and five heuristics were developed and applied to both problems. Computational experiments showed that the exact algorithms efficiently solved 14 out of 75 instances generated to the min-max regret WSCP and all realistic instances created to the min-max regret MCLP. For the simulated instances that was not solved to optimally in both problems, the heuristics developed in this thesis found solutions, as good as, or better than the best exact algorithm in almost all instance.
local.publisher.initialsUFMG

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