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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Logistically-constrained asset scheduling in maritime security operations

Clem, Doyne Damian. January 2008 (has links) (PDF)
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.

Dynamic programming speedups /

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

Analysis and optimization of complex nonserial dynamic programming network systems

Lee, Chae Young 05 1900 (has links)
No description available.

A comparative study and analysis of a class of dynamic programming algorithms

Ahn, Chul Woo 05 1900 (has links)
No description available.

Convergent surrogate-constraint dynamic programming.

January 2006 (has links)
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


Comeau, Jules 10 February 2011 (has links)
The main goal is to develop decision policies for individual forest stand management. It addresses three major areas of interest in the optimal management of individual forest stands: incorporating a two-species growth and yield model into a single stand management model, incorporating a comprehensive list of management options into a single stand management model, and incorporating uncertainty into a single stand management model. Dynamic programming (DP) is a natural framework to study forest management with uncertainty. The forest stand management problem, as modelled in this thesis, has a large dimensional state space with a mix of discrete and continuous state variables. The DP model used to study this problem is solved by value iteration with the objective of understanding infinite horizon policies. However, since some of the state variables are continuous, all states can’t be examined in an attempt to create the cost-to-go function. Therefore, the cost-to-go function value is calculated at a given stage of the algorithm at a finite set of state points and then the cost-to-go values are approximated on the continuous portion of the state space using a continuous function. All of this is done with random processes impacting state transitions. With the mixed-species growth model developed in this thesis, a comprehensive list of management options can be incorporated into the DP model and, with the addition of uncertainty from sources such as market prices and natural disasters, near optimal stand management policies are developed. Solving the DP model with the required level of detail lead to the development of insight into function fitting on continuous state spaces and to the development of cost-to-go function approximation bounds. Studying the policies shows that the addition of uncertainty to the model captures the dynamics between market prices and stand definitions, and leads to policies that are better suited to decision making in a stochastic environment, when compared with policies that are developed with a deterministic model. Enough precision is built into the DP model to give answers to typical questions forest managers would ask.

Investment and capacity choice under uncertain demand

Dangl, Thomas January 1999 (has links) (PDF)
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"

Automated translation of dynamic programming problems to Java code and their solution via an intermediate Petri net representation

Mauch, Holger January 2005 (has links)
Thesis (Ph. D.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 197-202). / Also available by subscription via World Wide Web / xi, 202 leaves, bound ill. 29 cm

Modeling and analyzing spread of epidemic diseases: case study based on cervical cancer

Parvin, Hoda 15 May 2009 (has links)
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.

Dynamic Pricing in a Competitive Environment

Perakis, Georgia, Sood, Anshul 01 1900 (has links)
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)

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