Constraint programming for optimization under uncertainty in inventory control

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dc.check.embargoformatEmbargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo)en
dc.check.infoNot applicableen
dc.check.opt-outNot applicableen
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dc.contributor.advisorPrestwich, Steven Daviden
dc.contributor.advisorTarim, S. Armaganen
dc.contributor.advisorHnich, Brahimen
dc.contributor.authorRossi, Roberto
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2018-04-25T10:41:43Z
dc.date.available2018-04-25T10:41:43Z
dc.date.issued2008
dc.date.submitted2008
dc.description.abstractConstraint Programming (CP) is a programming paradigm where relations between variables can be stated in the form of constraints. CP features discrete domains and global constraints. Global constraints capture interesting substructures of a problem, encapsulate dedicated inference algorithms based on feasibility and/or optimality reasoning, and provide information to the search process on the most viable course. Stochastic Constraint Programming (SCP) is a novel framework that generalizes CP to stochastic problems, allowing both to model and solve this class of problems by using any available existing CP solver. Although this framework proves to be extremely flexible in terms of modelling power, its current implementation does not scale well. In order to enhance this framework, in this dissertation we propose a general extension for SCP: global chance-constraints. In contrast to global constraints, which represent relations among a non-fixed number of decision variables, global chance-constraints represent relations among a non-fixed number of decision variables and stochastic variables. Nevertheless, as global constraints do, global chance-constraints encapsulate dedicated inference algorithms based on feasibility and/or optimality reasoning and may provide information to the search process. We call optimization-oriented global chance-constraints those global chance-constraints performing optimality reasoning. We applied global chance-constraints encapsulating dedicated inference algorithms based on feasibility and/or optimality reasoning to problems in the area of stochastic inventory control. Our computational experience shows that global chance-constraints let us model and solve to optimality problems that could not or could be only approximately solved by other existing approaches. It also shows that filtering based on optimality reasoning is extremely effective for this class of problems.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRossi, R. 2008. Constraint programming for optimization under uncertainty in inventory control. PhD Thesis, University College Cork.en
dc.identifier.endpage234en
dc.identifier.urihttps://hdl.handle.net/10468/5871
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Centre for Science Engineering and Technology (CSET)/03/CE3/I405/IE/CSET CTVR: Centre for Telecommunications Value Chain Driven Research (CTVR)/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/05/IN/I886/IE/Employing Artificial Intelligence to Make Constraint Programming Easier to Use for Decision Making/en
dc.rights© 2008, Roberto Rossi.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectConstraint programmingen
dc.subjectOptimizationen
dc.subjectInventory controlen
dc.thesis.opt-outtrue
dc.titleConstraint programming for optimization under uncertainty in inventory controlen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhDen
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