Solving Combinatorial Optimization Problems using Statistical Learning

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dc.contributor.authorNaguib, Andrew
dc.contributor.supervisorYousef, Waleed
dc.contributor.supervisorTraoré, Issa
dc.date.accessioned2023-04-28T20:19:35Z
dc.date.available2023-04-28T20:19:35Z
dc.date.copyright2023en_US
dc.date.issued2023-04-28
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractThis thesis examines the use of geometric deep neural networks to provide competent solutions (in terms of runtime versus duality gap), not necessarily incumbent, to the capacitated vehicle routing problem and the bin packing problem—which have non-deterministic polynomial computational complexity. The core idea is based on learning to approximate the decisions made by the branch and bound algorithm using the computationally expensive strong branching strategy. The classifiers - graph convolutional neural network, Graph- SAGE, and graph attention network - are trained on six topologically different (to investigate the geographical dispersion effect on optimality) instances and evaluated on eight additional instances. The experiments we conducted show that the proposed approach is able to match the performance of the branch and bound algorithm and improve upon it on two different branching strategies, while requiring significantly less computation time and explored branching nodes.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/15055
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectcombinatorial optimizationen_US
dc.subjectvehicle routing problemen_US
dc.subjectbin packing problemen_US
dc.subjectgeometric deep learningen_US
dc.titleSolving Combinatorial Optimization Problems using Statistical Learningen_US
dc.typeThesisen_US

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