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dc.contributor.authorHu, Weici
dc.date.accessioned2018-04-26T14:17:49Z
dc.date.available2018-04-26T14:17:49Z
dc.date.issued2017-08-30
dc.identifier.otherHu_cornellgrad_0058F_10530
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:10530
dc.identifier.otherbibid: 10361629
dc.identifier.urihttps://hdl.handle.net/1813/56952
dc.description.abstractWe consider a class of stochastic sequential allocation problems - restless multi-armed bandits (RMAB) with a finite horizon and multiple pulls per period. Leveraging the Lagrangian relaxation of the problem, we propose an index-based policy that uses the optimal Lagrange multipliers to index individual arms, and prove that the policy is asymptotically optimal as the number of arms tends to infinity. We also demonstrate numerically that this index-based policy outperforms state-of-the-art heuristics in several instances of RMAB. In addition, we study two other applications of sequential resource allocation problems which are extensions of the RMAB problem, and demonstrate how our index policy can be adapted to these settings.
dc.language.isoen_US
dc.subjectIndex-based Policy
dc.subjectRestless Bandit
dc.subjectSequential Resource Allocation
dc.subjectStochastic Dynamic Program
dc.subjectOperations research
dc.titleSequential Resource Allocation Under Uncertainty: An Index Policy Approach
dc.typedissertation or thesis
thesis.degree.disciplineOperations Research
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Operations Research
dc.contributor.chairFrazier, Peter
dc.contributor.committeeMemberTopaloglu, Huseyin
dc.contributor.committeeMemberJoachims, Thorsten
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/X4HX19VJ


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