We propose a resource allocation model for the management of the fund for the screening and treatment of women infected by Chlamydia trachomatis and Neisseria gonorrhoeae. The goal is to maximize the number of infected women cured of Chlamydia trachomatis and Neisseria gonorrhoeae infections. The population going for screening is divided into groups by ages and races. The group number is dynamic. Dierent groups have dierent infection rates. There are four possible test assays and four possible treatments. We employed a two-phase algorithm to solve the problem. The first phase is small so an exhaustive method is applied, while the second phase is transformed to a knapsack problem and a dynamic programming method is applied.

The ground conditions prevailing on the day of a cricket match is an important confounding variable that results in the majority of cricket analyses requiring qualification. We present a Bayesian method for estimating the value of ground conditions in the absence of a direct measure. We use dynamic programming techniques to estimate models of both the first and second innings and we outline an application for each model. We extract a proxy variable for risk from our first-innings model and we use this variable to successfully estimate the trade-off between scoring rate and the probability of survival for individual batsmen. This enables us to decompose a batsman’s performance into ability and strategic nous. Our second-innings model gives an estimate of a team’s probability of winning at any point in the second innings of the match. We use this variable in conjunction with our ground-conditions variable to outline a new method for adjusting the target score in rain-affected matches. We introduce a simple metric for comparing the performance of various rain rules and we find that our proposed rule outperforms the incumbent Duckworth/Lewis method.

Today's dynamic language systems have grown to include features that resemble features of operating systems. It may be possible to improve on both by unifying a language system with an operating system. Complete unification does not appear possible in the near-term, so an intermediate system is described. This intermediate system uses a common call graph to allow components in arbitrary languages to interact as easily as components in the same language. Potential benefits of such a system include significant improvements in interoperability, improved reusability and backward compatibility, simplification of debugging and some administrative tasks, and distribution over a cluster without any changes to application code.

The thesis focuses on a model that seeks to address patient scheduling step of the surgical scheduling process to determine the number of surgeries to perform in a given day. Specifically, provided a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, we look to provide a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the ORs and congestion in the wards. We cast the problem of generating optimal control strategies into the framework of Markov Decision Process (MDP). The Approximate Dynamic Programming (ADP) approach has been employed to solving the model which would otherwise be intractable due to the size of the state space. We assess performance of resulting policy and quality of the driven policy through simulation and we provide our policy insights and conclusions.

In the near future, demand for Heterogeneous Wireless Networking (HWN) is expected to increase. HWNs are formed by integration of different communication technologies, for example the integration of wireless LAN and cellular networks, to support mobile users. QoS provisioning in these networks is a challenging issue given the diversity in wireless technologies and the existence of mobile users with different communication requirements. In this thesis, we consider optimal resource planning and dynamic resource management for HWNs. In the first part of this thesis, we examine the optimal deployment of such networks. We propose a mobility-aware network planning optimization in which the objective is to minimize the rate of upward vertical handovers while maximizing the total number of users accommodated by the network. The optimal placement of Access Points (AP) with respect to these two objectives is formulated as an integer programming problem. Our results show that considering the mobility pattern in the planning phase of network deployment can significantly improve infrastructure performance. In the second part, we investigate optimal admission control policies employed in maintaining QoS in HWNs. Here we consider two cases: integration of cellular overlay with a single WLAN AP, and integration with a WLAN mesh network. A decision theoretic framework for the problem is derived using a dynamic programming formulation. In the case of single WLAN AP and cellular overlay, we prove that for this two-tier wireless network architecture, the optimal policy has a two-dimensional threshold structure. Furthermore, this structural result is used to design two computationally efficient algorithms, Structured Value Iteration and Structured Update Value Iteration. These algorithms can be used to determine the optimal policy in terms of thresholds. Although the first one is closer in its operation to the conventional Value Iteration algorithm, the second one has a significantly lower complexity. In the second case where the underlay is a complex WLAN mesh network, we develop a Partially Observable Markov-Modulated Poisson Process (PO-MMPP) traffic model to characterize the overflow traffic from the underlaying mesh to the overlay. This model captures the burstiness of the overflow traffic under the imperfect observability of the mesh network states. Then, by modeling the overlay network as a controlled PO-MMPP/M/C/C queueing system and obtaining structured decision theoretic results, it is shown that the optimal control policy for this class of HWNs can be characterized as monotonic \emph{threshold curves}. Moreover, these results are used to design a computationally efficient algorithm to determine the optimal policy in terms of thresholds. Extensive numerical observations suggest that, in both cases and for all practical parameter sets, the algorithms converge to the overall optimal policy. Additionally, numerical results show that the proposed algorithms are efficient in terms of time-complexity and in achieving optimal performance by significantly reducing the probability of dropped and blocked calls.

In the near future, demand for Heterogeneous Wireless Networking (HWN) is expected to increase. HWNs are formed by integration of different communication technologies, for example the integration of wireless LAN and cellular networks, to support mobile users. QoS provisioning in these networks is a challenging issue given the diversity in wireless technologies and the existence of mobile users with different communication requirements. In this thesis, we consider optimal resource planning and dynamic resource management for HWNs. In the first part of this thesis, we examine the optimal deployment of such networks. We propose a mobility-aware network planning optimization in which the objective is to minimize the rate of upward vertical handovers while maximizing the total number of users accommodated by the network. The optimal placement of Access Points (AP) with respect to these two objectives is formulated as an integer programming problem. Our results show that considering the mobility pattern in the planning phase of network deployment can significantly improve infrastructure performance. In the second part, we investigate optimal admission control policies employed in maintaining QoS in HWNs. Here we consider two cases: integration of cellular overlay with a single WLAN AP, and integration with a WLAN mesh network. A decision theoretic framework for the problem is derived using a dynamic programming formulation. In the case of single WLAN AP and cellular overlay, we prove that for this two-tier wireless network architecture, the optimal policy has a two-dimensional threshold structure. Furthermore, this structural result is used to design two computationally efficient algorithms, Structured Value Iteration and Structured Update Value Iteration. These algorithms can be used to determine the optimal policy in terms of thresholds. Although the first one is closer in its operation to the conventional Value Iteration algorithm, the second one has a significantly lower complexity. In the second case where the underlay is a complex WLAN mesh network, we develop a Partially Observable Markov-Modulated Poisson Process (PO-MMPP) traffic model to characterize the overflow traffic from the underlaying mesh to the overlay. This model captures the burstiness of the overflow traffic under the imperfect observability of the mesh network states. Then, by modeling the overlay network as a controlled PO-MMPP/M/C/C queueing system and obtaining structured decision theoretic results, it is shown that the optimal control policy for this class of HWNs can be characterized as monotonic \emph{threshold curves}. Moreover, these results are used to design a computationally efficient algorithm to determine the optimal policy in terms of thresholds. Extensive numerical observations suggest that, in both cases and for all practical parameter sets, the algorithms converge to the overall optimal policy. Additionally, numerical results show that the proposed algorithms are efficient in terms of time-complexity and in achieving optimal performance by significantly reducing the probability of dropped and blocked calls.

In the field of proteomic mass spectrometry, proteins can be sequenced by two independent yet complementary algorithms: de novo sequencing which uses no prior knowledge and database search which relies upon existing protein databases. In the case where an organism’s protein database is not available, the software Spider was developed in order to search sequence tags produced by de novo sequencing against a database from a related organism while accounting for both errors in the sequence tags and mutations. This thesis further develops Spider by using the concept of reconstruction in order to predict the real sequence by considering both the sequence tags and their matched homologous peptides. The significant value of these reconstructed sequences is demonstrated. Additionally, the runtime is greatly reduced and separated into independent caching and matching steps. This new approach allows for the development of an efficient algorithm for search. In addition, the algorithm’s output can be used for new applications. This is illustrated by a contribution to a complete protein sequencing application.

Dynamic Programming is an optimization technique used in computer science and mathematics. Introduced in the 1950s, it has been applied to many classic combinatorial optimization problems, such as the Shortest Path Problem, the Knapsack Problem, and the Traveling Salesman Problem, with varying degrees of practical success. In this thesis, we present two applications of dynamic programming to optimization problems. The first application is as a method to compute the Branch-Cut-and-Price (BCP) family of lower bounds for the Traveling Salesman Problem (TSP), and several vehicle routing problems that generalize it. We then prove that the BCP family provides a set of lower bounds that is at least as strong as the Approximate Linear Program (ALP) family of lower bounds for the TSP. The second application is a novel dynamic programming model used to determine the placement of cuts for a particular form of skull surgery called Cranial Vault Remodeling.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 104-107).

Thesis (M.S.)--Ohio University, August, 2001. / Title from PDF t.p.