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This dissertation addresses a class of fundamental logistical problems where two or more potential users (or players) compete for a common set of resources. Each user has a criterion (cost or performance requirement) that he/she wishes to optimize. The users' criteria are often in conflict, that is, choosing a decision that optimizes one user's criterion may not also optimize the criteria of others. How should the resources be utilized to satisfy the user demands? In this dissertation, optimization and game theoretical models are employed to examine the equilibrium points and efficiently find the frontier of non-dominated solutions to three logistics problems with competing users: (1) single machine scheduling, (2) network resource allocation and (3) assignment to multiple servers (queues). New cooperative game theoretic methods are developed to negotiate on the Pareto frontier. In addition, a Stackelberg leader-follower game framework is introduced in a queueing system which includes both competitive users and competitive servers. The existence of a unique equilibrium is shown. The models and methodologies developed in the dissertation can be applied in many areas, such as Internet pricing, scheduling resources among competitors, network routing of users' requirements, analysis of competitive market, etc.

First, this thesis presents four types of dynamic models for one-link flexible manipulators: Euler-Bernoulli model, Euler-Bernoulli model with rotatory inertia, Timoshenko model, and Euler-Bernoulli model with tip mass through Hamilton Principle, and exact modal frequencies and vibration modes are derived through Laplace transformation and eigenanalysis. The numerical analysis is conducted to verify the established models and investigate the influences of rotatory inertia, shear deformation and tip mass. Second, this thesis presents the exact dynamic solutions of Euler-Bernoulli model for the step torque input and design of a dynamics simulator for one-link flexible manipulators based on the exact dynamic solutions. Several types of sensor models have been specified in the designed simulator to provide various sensory data sets required for feedback controls. A simulation study is performed to verify the designed simulator and to demonstrate the usage of the dynamics simulator for controller design and evaluations.

The distributed processing capabilities of local area networks has led to the development of "client-server" applications for microcomputers. Two separate computer programs, the client and the server, comprise the client-server system. The server program receives and services requests sent to it by the client program. More than one copy of the client program may be connected at a time to the server via the network with each instance of the client program operating on a separate computer. Two difficulties are encountered when testing such a system. First, since each client station is operated by one or more human-users, extensive evaluation is costly when several clients are operated for a substantial amount of time. A second problem is an inherent inaccessibility to information about internal system states. This thesis presents a method for automating performance evaluations of the server component of the client-server software system. Two changes to the original system are required. First, the human-user inputs to the system are generated using probability distributions. Second, an additional computer program, the test monitor, is added to the system in order to automate the accumulation and storage of test data.

In this dissertation, the class of problems where a performance index must be optimized at the final time of operation of a batch process, is studied for various process conditions. First, optimal state feedback laws for end-point optimization of dynamic systems are derived where the state model is a nonlinear function of the manipulated input and the system states. The necessary conditions for optimality are cast in terms of the system Lie brackets and the adjoint states. An optimal state feedback law is derived which is independent of adjoint states. The nature of the optimal state feedback law (static or dynamic) is characterized in terms of the system dynamics. In the next phase of this work, this optimal feedback approach is extended to include the effect of measurable disturbances. It is found that depending on the degree of singularity with respect to manipulated input and/or disturbance input, the feedforward/feedback laws are either static or dynamic. In the final phase of this research, optimal state feedback laws are derived for on-line optimization of batch processes with two manipulated inputs where one input appears nonlinearly and the other appears linearly in the state model. As illustrative examples of application of the proposed state feedback laws, several end-point optimization problems in batch chemical reactors are considered. / Source: Dissertation Abstracts International, Volume: 57-03, Section: B, page: 1964. / Major Professor: Srinivas Palanki. / Thesis (Ph.D.)--The Florida State University, 1996.

For any nontrivial system or a group of such systems to operate harmoniously, there must exist protection policies, which outline what are allowed, as well as what should not be done. The complexity of the protection policies is proportional to the complexity of the systems involved and the connections between those systems. Due to continuous development of new technologies, especially the information technology, the complexity of most systems, as well as that of the connections between them, constantly increases. Consequently, there is a need for a framework in which such complex protection policies can be systematically analyzed. / A new concept of Connected Protection Structures (CPSs) is developed based on the original capability-pass-permit model of dynamic protection structure of Kohout and Gaines. The structures are defined in terms of fuzzy relations. CPSs provide a needed framework for the analysis of protection policies of a given group of connecting systems. A stand-alone system can also be dealt with by the CPSs as a special case. The main objective of the analysis is to uncover any possible harmful actions that are allowed but should not be performed, i.e. potential violations. CPSs take into account both internal protection policies which govern interactions within one system, and external protection policies which govern interactions across connecting systems. Computational techniques that lead to the determination of potential violations, and a software that implements the techniques are developed. Ways to construct CPSs from given protection policies, as well as a suggestion on how to incorporate the resulting CPSs into a protection mechanism for a generic application, are also presented. / Source: Dissertation Abstracts International, Volume: 52-10, Section: B, page: 5377. / Major Professor: Ladislav J. Kohout. / Thesis (Ph.D.)--The Florida State University, 1991.

The first chapter of this dissertation estimates the relative contributions of two major exchanges on crude oil futures to the price discovery process-- Chicago Mercantile Exchange (CME) and Intercontinental Exchange (ICE), using trade-by-trade data in 2008. The study also empirically analyzes the effects of trading characteristics on the information share of these two markets. Trading characteristics examined in the study include trading volume, trade size, and trading costs. On average, CME is characterized by greater volume and trade size but also slightly greater bid-ask spread. CME leads the process of price discovery and this leadership is caused by relative trade size and volatility before the financial crisis of 2008; however post-crisis period this leadership is caused by trading volume. Moreover, this study presents evidence that, in times of large uncertainty in the market, the market maker charges a greater bid-ask spread for the more informative market. / The second chapter examines the influence of expected oil price volatility, the behavior of the Organization of Petroleum Exporting Countries (OPEC), and the US Dollar exchange rate volatility on the backwardation of crude oil futures during the period from January 1986 to December 2008. The results indicate that oil futures are strongly and weakly backwardated 57% and 69% of the time, respectively. The regression analysis of weak backwardation shows that oil volatility, OPEC overproduction (difference between quota and the actual production), and the volatility of the US Dollar against the Japanese Yen have a positive significant effect on oil backwardation, while OPEC production quota imposed on its members has a negative significant effect on oil backwardation. However the volatility of US Dollar against the British Pound has no significant effect on oil backwardation. The regression analysis of strong backwardation produces qualitatively the same results except that volatility has no effect. In a sub-period analysis, evidence also indicates that trading volume of oil funds and backwardation are negatively related, suggesting that oil funds increase the demand of futures relative to that of spot. / Keywords: Oil futures, price discovery, trading characteristics, bid-ask spread, financial crisis, backwardation, OPEC, oil funds, and exchange rate.

In this dissertation, a stochastic programming model is presented for multi-scale decision-oriented dynamic systems (DODS) which are discrete-time systems in which decisions are made according to alternative discrete-time sequences which depend upon the organizational layer within a hierarchical system. A multi-scale DODS consists of multiple modules, each of which makes decisions on a time-scale that matches their specific task. For instance, in a large production planning system, the aggregate planning module may make decisions on a quarterly basis, whereas, weekly, and daily planning may use short-term scheduling models. In order to avoid mismatches between these schedules, it is important to integrate the short-term and long-term models. In studying models that accommodate multiple time-scales, one of the challenges that must be overcome is the incorporation of uncertainty. For instance, aggregate production planning is carried out several months prior to obtaining accurate demand estimates. In order to make decisions that are cognizant of uncertainty, we propose a stochastic programming model for the multi-scale DODS. Furthermore, we propose a modular algorithm motivated by the column generation decomposition strategy. The convergence of this modular algorithm is also demonstrated. Our experimental results demonstrate that the modular algorithm is robust in solving large-scale multi-scale DODS problems under uncertainty. Another main issue addressed in this dissertation is the application of the above modeling method and solution technique to decision aids for scheduling and hedging in a deregulated electricity market (DASH). The DASH model for power portfolio optimization provides a tool which helps decision-makers coordinate production decisions with opportunities in the wholesale power market. The methodology is based on a multi-scale DODS. This model selects portfolio positions for electricity and fuel forwards, while remaining cognizant of spot market prices, and generation costs. When compared with a commonly used fixed-mix policy, our experiments demonstrate that the DASH model provides significant advantages over fixed-mix policies. Finally, a multi-level distributed computing system is designed in a manner that implements the nested column generation decomposition approach for multi-scale decision-oriented dynamic systems based on a nested column generation decomposition approach. The implementation of a three-level distributed computing system is discussed in detail. The computational experiments are based on a large-scale real-world problem arising in power portfolio optimization. The deterministic equivalent LP for this instance with 200 scenarios has over one million constraints. Our computational results illustrate the effectiveness of this distributed computing approach.

Interest in photovoltaic products has expanded dramatically, but wide-scale commercial use remains limited due to the high manufacturing cost and insufficient efficiency of solar products. Therefore, it is critical to develop effective process monitoring, diagnosing, and control methods for quality and productivity improvement. This dissertation is motivated by this timely need to develop effective process control methods for variation reduction in thin film solar cell manufacturing processes. Three fundamental research issues related to process monitoring, diagnosis, and control have been studied accordingly. The major research activities and the corresponding contributions are summarized as follows: (1) Online SPC is integrated with generalized predictive control (GPC) for the first time for effective process monitoring and control. This research emphasizes on the importance of developing supervisory strategies, in which the controller parameters are adaptively changed based on the detection of different process change patterns using SPC techniques. It has been shown that the integration of SPC and GPC provides great potential for the development of effective controllers especially for a complex manufacturing process with a large time varying delay and different process change patterns. (2) A generic hierarchical ANOVA method is developed for systematic variation decomposition and diagnosis in batch manufacturing processes. Different from SPC, which focuses on variation reduction due to assignable causes, this research aims to reduce inherent normal process variation by assessing and diagnosing inherent variance components from production data. A systematic method of how to use a full factor decomposition model to systematically determine an appropriate nested model structure is investigated for the first time in this dissertation. (3) A Multiscale statistical process monitoring method is proposed for the first time to simultaneously detect mean shift and variance change for autocorrelated data. Three wavelet-based monitoring charts are developed to separately detect process variance change, measurement error variance change, and process mean shift simultaneously. Although the solar cell manufacturing process is used as an example in the dissertation, the developed methodologies are generic for process monitoring, diagnosis, and control in process variation reduction, which are expected to be applicable to various other semiconductor and chemical manufacturing processes.