Investigation of matching problems using constraint programming and optimisation methods

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dc.contributor.advisor O'Sullivan, Barry en
dc.contributor.author Chisca, Danuta Sorina
dc.date.accessioned 2020-05-12T10:00:09Z
dc.date.available 2020-05-12T10:00:09Z
dc.date.issued 2019-11
dc.date.submitted 2019-11
dc.identifier.citation Chisca, D. S. 2019. Investigation of matching problems using constraint programming and optimisation methods. PhD Thesis, University College Cork. en
dc.identifier.endpage 153 en
dc.identifier.uri http://hdl.handle.net/10468/9921
dc.description.abstract This thesis focuses on matching under ordinal preferences, i.e. problems where agents may be required to list other agents that they find acceptable in order of preference. In particular, we focus on two main cases: the popular matching and the kidney exchange problem. These problems are important in practice and in this thesis we develop novel algorithms and techniques to solve them as combinatorial optimisation problems. The first part of the thesis focuses on one-sided matching on a bipartite graph, specifically the popular matching. When the participants express their preferences in a cardinal order, the most common desire is to maximise a utility function, for example finding the maximum weight or minimum cost matching. If preferences are ordinal in nature, one might want to guarantee that no two applicants are inclined to form a coalition in order to maximise their welfare, thus finding a stable matching is needed. Therefore, negotiating the size and the optimality of the matching with respect to agents’ preferences is a problem that occurs naturally. Popularity is a concept that offers an attractive trade- off between these two notions. In short, a popular matching is a matching in which the majority of the participants will decide on a matching M . In particular, we examine the popular matching in the context of constraint programming using global constraints. We discuss the possibility to find a popular matching even for the instances that does not admit one. The second part of the thesis focuses on non-bipartite graphs, i.e. the kidney exchange problem, a recent innovation that matches patients in need of a kidney to willing living donors. Kidney transplant is the most effective treatment to cure end-stage renal disease, affecting one in every thousand European citizens. Chronic kidney disease is a life threatening health issue that affects millions of people worldwide. Motivated by the observation that the kidney exchange is inherently a stochastic online problem, first, we give a stochastic online method. This methos is more general and provides an expected value estimation that is correct within the limit of sampling errors. Sec- ond, we show that by taking into consideration a probabilistic model of future arrivals and drop-offs, we can get reduce sampling scenarios, and we can even construct a sampling-free probabilistic model, called the Abstract Exchange Graph (AEG). A final contribution of this thesis is related to finding robust solutions when uncertainty occurs. Uncertainty is inherent to most real world problems. For instance in the case of Kidney Exchange Problem (KEP), if a pair pools out from the solution we need to find a repair solution quickly to limit the impact to other pairs. We propose a method that exploits a Logic-Based Benders Decomposition to find super solutions to an optimisation problem. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.rights © 2019, Danuta Sorina Chisca. en
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Optimisation en
dc.subject Kidney exchanges problem en
dc.subject Matching problems en
dc.subject Stochastic optimisation en
dc.title Investigation of matching problems using constraint programming and optimisation methods en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD - Doctor of Philosophy en
dc.internal.availability Full text available en
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.description.status Peer reviewed en
dc.internal.school Computer Science and Information Technology en
dc.internal.conferring Summer 2020 en
dc.internal.ricu Insight - Centre for Data Analytics en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en
dc.contributor.advisorexternal Milano, Michela en
dc.availability.bitstream openaccess


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© 2019, Danuta Sorina Chisca. Except where otherwise noted, this item's license is described as © 2019, Danuta Sorina Chisca.
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