Essays in problems of optimal sequential decisions [electronic resource].

Arlotto, Alessandro.
150 p.
Contained In:
Dissertation Abstracts International 74-05B(E).

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Operations research.
Business Education.
Local subjects:
Education, Business. (search)
Operations Research. (search)
Penn dissertations -- Operations and information management. (search)
Operations and information management -- Penn dissertations. (search)
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Mode of access: World Wide Web.
In this dissertation, we study several Markovian problems of optimal sequential decisions by focusing on research questions that are driven by probabilistic and operations-management considerations. Our probabilistic interest is in understanding the distribution of the total reward that one obtains when implementing a policy that maximizes its expected value. With this respect, we study the sequential selection of unimodal and alternating subsequences from a random sample, and we prove accurate bounds for the expected values and exact asymptotics. In the unimodal problem, we also note that the variance of the optimal total reward can be bounded in terms of its expected value. This fact then motivates a much broader analysis that characterizes a class of Markov decision problems that share this important property. In the alternating subsequence problem, we also outline how one could be able to prove a Central Limit Theorem for the number of alternating selections in a finite random sample, as the size of the sample grows to infinity. Our operations-management interest is in studying the interaction of on-the-job learning and learning-by-doing in a workforce-related problem. Specifically, we study the sequential hiring and retention of heterogeneous workers who learn over time. We model the hiring and retention problem as a Bayesian infinite-armed bandit, and we characterize the optimal policy in detail. Through an extensive set of numerical examples, we gain insights into the managerial nature of the problem, and we demonstrate that the value of active monitoring and screening of employees can be substantial.
Thesis (Ph.D. in Operations and Information Management) -- University of Pennsylvania, 2012.
Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.
Advisers: Noah Gans; J. Michael Steele.
Local notes:
School code: 0175.
Chick, Stephen E. committee member
Savin, Sergei, committee member
Steele, J. Michael, advisor
Gans, Noah, advisor
University of Pennsylvania. Operations and Information Management.
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