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dc.contributor.advisorPodorozhny, Rodion
dc.contributor.authorShiraz, Muhammad Asif
dc.date.accessioned2016-06-23T17:59:27Z
dc.date.available2016-06-23T17:59:27Z
dc.date.created2016-05
dc.date.issued2016-04-25
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/6074
dc.description.abstractScheduling complex problem solving tasks where tasks are interrelated and there are multiple different ways to go about achieving a particular task is a computationally challenging problem. In this thesis, we study current approaches to solving such complex scheduling problems, and propose two new optimization techniques, which exploit A* based optimization, and constraint based optimization. We then perform an analytical comparison and computational complexity estimate for the efficiency enhancement achieved by these approaches, as compared against a base line case of “god’s view” based optimal policy evaluation for same problems.
dc.formatText
dc.format.extent68 pages
dc.format.medium1 file (.pdf)
dc.language.isoen_US
dc.subjectScheduling
dc.subjectTeams
dc.subjectDistributed Constraint Optimization
dc.subject.lcshComputational Intelligenceen_US
dc.subject.lcshConstraint programming (Computer science)en_US
dc.subject.lcshTask analysisen_US
dc.subject.lcshScheduling--Data processingen_US
dc.titleEvaluation and Adaptation of a Constraint Optimization and Distributed, Anytime A* Algorithm to Design-To-Criteria Scheduling Problem
txstate.documenttypeThesis
dc.contributor.committeeMemberYang, Guowei
dc.contributor.committeeMemberGuirguis, Mina S.‎
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineSoftware Engineeringen_US
thesis.degree.grantorTexas State Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
txstate.departmentComputer Science


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