Wang Qing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 72-74). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature survey --- p.2 / Chapter 1.2 --- Research carried out in this thesis --- p.4 / Chapter 2 --- Conventional Dynamic Programming --- p.7 / Chapter 2.1 --- Principle of optimality and decomposition --- p.7 / Chapter 2.2 --- Backward dynamic programming --- p.12 / Chapter 2.3 --- Forward dynamic programming --- p.15 / Chapter 2.4 --- Curse of dimensionality --- p.19 / Chapter 2.5 --- Singly constrained case --- p.21 / Chapter 3 --- Surrogate Constraint Formulation --- p.24 / Chapter 3.1 --- Conventional surrogate constraint formulation --- p.24 / Chapter 3.2 --- Surrogate dual search --- p.26 / Chapter 3.3 --- Nonlinear surrogate constraint formulation --- p.30 / Chapter 4 --- Convergent Surrogate Constraint Dynamic Programming: Objective Level Cut --- p.38 / Chapter 5 --- Convergent Surrogate Constraint Dynamic Programming: Domain Cut --- p.44 / Chapter 6 --- Computational Results and Analysis --- p.60 / Chapter 6.1 --- Sample problems --- p.61 / Chapter 7 --- Conclusions --- p.70

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2008. / Thesis Advisor(s): Royset, Johannes O. "September 2008." Description based on title screen as viewed on November 5, 2008. Includes bibliographical references (p. 37-38). Also available in print.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 92-101). Also available in electronic version.

We present a dynamic optimization approach for perishable products in a competitive and dynamically changing market. We build a general optimization framework that ties together the competetive and the dynamic nature of pricing. This approach also allows differential pricing for large customers as well as demand learning for the seller. We analyze special cases of the model and illustrate the policies numerically. / Singapore-MIT Alliance (SMA)

In this thesis, health care policy issues for prevention and cure of cervical cancer have been considered. The cancer is typically caused by Human Papilloma Virus (HPV) for which individuals can be tested and also given vaccinations. Policymakers are faced with the decision of how many cancer treatments to subsidize, how many vaccinations to give and how many tests to be performed in each period of a given time horizon. To aid this decision-making exercise, a stochastic dynamic optimal control problem with feedback was formulated, which can be modeled as a Markov decision process (MDP). Solving the MDP is, however, computationally intractable because of the large state space as the embedded stochastic network cannot be decomposed. Hence, an algorithm was proposed that initially ignores the feedback and later incorporates it heuristically. As part of the algorithm, alternate methodologies, based on deterministic analysis, were developed, Markov chains and simulations to approximately evaluate the objective function. Upon implementing the algorithm using a meta-heuristic for a case study of the population in the United States, several measures were calculated to observe the behavior of the system through the course of time, based on the different proposed policies. The policies compared were static, dynamic without feedback and dynamic with feedback. It was found that the dynamic policy without feedback performs almost as well as the dynamic policy with feedback, both of them outperforming the static policy. All these policies are applicable and fast for easy what-if analysis for the policymakers.

This paper extends the real options literature by discussing an investment problem, where a firm has to determine optimal investment timing and optimal capacity choice at the same time under conditions of irreversible investment expenditures and uncertainty in future demand. After the project is installed with a certain maximum capacity, this capacity is fixed as an upper boundary to the output and cannot be adjusted later on. It turns out that, in the framework of this once and for all decision, uncertainty in future demand leads to an increase in optimal installed capacity. But on the other hand it causes investment to be delayed to an extent that even small uncertainty makes waiting and accumulation of further information the optimal decision for large ranges of demand. Limiting the capacity which may be installed weakens this extreme effect of uncertainty. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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