Thesis--Ph. D., University of Hong Kong. / Mimeographed. Also available on microfilm.

Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Nemhauser, George; Committee Co-Chair: Ahmed, Shabbir; Committee Member: Bill Cook; Committee Member: Gu, Zonghao; Committee Member: Johnson, Ellis. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 201-205). Also available in electronic version. Access restricted to campus users.

Energy harvesting in cellular networks is an emerging technique to enhance the sus- tainability of power-constrained wireless devices. In this thesis, I consider the co- channel deployment of a macrocell overlaid with several small cells. In our model, the small cell base stations (SBSs) harvest their energy from environment sources (e.g., solar, wind, thermal) whereas the macrocell base station (MBS) uses conven- tional power supply. Given a stochastic energy arrival process, a power control policy for the downlink transmission of both MBS and SBSs is derived such that they can obtain their own objectives on a long-term basis (e.g., maintain the target signal-to- interference-plus-noise ratio [SINR] on a given transmission channel). To this end, I propose to use two di erent forms of stochastic game for the cases when the number of SBSs is small and when it becomes very large i.e. a very dense network. Numerical results demonstrate the signi cance of the developed optimal power control policy in both cases over the conventional methods.

This thesis takes as its objective quantifying, comparing, and optimizing multiple-antenna (MIMO) physical layer techniques in dense ad hoc wireless networks. A framework is developed from the spatial shot noise interference model for packet radio network analysis. The framework captures the behavior of a wide variety of signal and interference distributions, which permit inspection of a number of signal processing methods including representatives from most of the major MIMO techniques. Multi-antenna systems for point-to-point are becoming mature and being developed and deployed in many wireless communication systems due to their potential to combat fading, increase spectral efficiency, and overcome interference. The framework permits an algorithm or system designer to view the network from the perspective of a typical user, to optimize performance in the midst of a given environment, or to view the network as a whole, to determine behavior that maximizes network performance. In particular, it enables questions to be answered quantitatively, such as which MIMO techniques perform best in a given environment? Or what rate and power settings should be used across the available spatial modes? Or what is the maximum benefit of channel state information? Or what gain should an individual device, or the network as a whole expect to see given a particular physical layer strategy? The dissertation begins by developing the framework for a generic set of assumptions on network behavior and signal and interference distributions. It then presents a progression of applications to representative MIMO techniques. Broad and intuitive scaling laws are developed as well as detailed exact results for careful comparison. Capacity scaling with the number of antennas is given for systems employing beamforming, selection combining, space-time block coding, and spatial multiplexing. These applications are used as the basis for developing simple distributed algorithms for optimizing MIMO settings with QoS constraints and in heterogeneous networks. Lastly, the framework is expanded to permit comparison and optimization of MIMO performance under alternative medium access strategies. In general it is found that significant performance gains can be reaped with multi-antenna physical layers, provided the proper techniques are employed. It is also shown that the availability of multiple spatial channels impacts the inherent tradeoff between per-link throughput and spatial reuse. / text

This dissertation proposes a new transmission planning method for electric power systems with large planned additions of uncertain intermittent renewable resources. The major contribution of this dissertation is applying stochastic programming that represents two uncertain parameters, wind and load, to transmission planning. We apply an ad hoc partition method to approximate the bivariate random variables of load and wind. A two-stage stochastic transmission planning problem is repeatedly solved by replacing continuous random variables with approximations that have a more refined partition at each iteration. A candidate solution is provided when improvement is not observed at an optimal value, even with more refined approximations. Numerical results show the efficiency of the method. However, if the number of samples is not sufficient to represent the original random variable's characteristics, the solution may be poor. Therefore, we employ a sampling method using Gaussian copula in order to generate as many random samples as necessary. The problem is replicated and solved using a fixed number of samples generated by Gaussian copula. In order to asses solution quality, a 95\%-confidence interval on the optimality gap is formed. A candidate stochastic solution for transmission investment is used to simulate the operation of a utility-scale storage system. A mixed integer program (MIP) is applied to this formulation. As a case study, the Electric Reliability Council of Texas (ERCOT) wind and load data is employed, along with a simplified model of the transmission system. Energy storage is also considered. The storage operation shifts wind power from off-peak hours to on-peak hours, and its wind power generation shows a close character to that of a base load generator. / text

published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy

Timely provision of quality products at the lowest prices possible has become the utmost competitive edge being pursued by virtually all manufacturing firms. They endeavour to speed up their production and deliveries of goods to end customers in order to make more money and even survive in the fierce competition arena. Although much progress has been made in operations management and a series of production planning approaches have been proposed to achieve various manufacturing operations goals, optimisation results are often rendered unrealistic and even misleading, for few studies have considered the overall corporate goal of shareholder wealth maximisation and the specific economic environments where manufacturing firms operate. Some critical factors closely related to interests of corporate owners, such as working capital management and capital structure, are rarely involved in an overwhelming majority of production planning problems. Moreover, the overlook of the effects of production planning results on the environment makes them more impractical and even unavailable in real-world manufacturing environments. To this end, the dissertation proposes a stochastic production planning model for the uncertain make-to-order production environment, with the focus mainly on the lot sizing decision-making policy. The primary goal of the optimization problem is to maximise the sustainable full interests of corporate owners, namely, the shareholder wealth, rather than to optimise some traditional local or short-term objective functions, such as work flow times, accounting costs, accounting profits and the like. To improve the generality and exactness of the proposed model, all involved uncertain random events are characterized by their own inherent statistical merits without any impractical assumptions on their distributions. The improvement of production planning is not the only one single source of the wealth-based business performance. There are also some other critical factors which can impose direct influences on shareholder wealth. Among these potential shareholder wealth drivers, we choose to examine the effective management of working capital and capital structure, for they are closely pertinent to a firm’s financial position and its cash flow status. In addition, environmental protection has in recent decades aroused extensively global attention because of its far-reaching impingements on the social and economic developments of the world. The carbon emission in production, especially its main component—carbon dioxide, is generally recognized as the most important emission source. To mitigate their diverse interference with the climate and the environment, a wide range of emission reduction measures, laws, and legislations has been enacted and implemented, making production planning optimisations more complicated. To better reflect the emerging production planning environment facing manufacturing firms, the emission trading system for carbon management, which has thus far become the most popular market-based carbon reduction mechanism, is incorporated into the proposed production planning model. To theoretically and analytically validate the proposed approach, the probability and convex theories are adopted to prove the convexity or concavity of the optimisation objectives and the relevant global optimal characteristics. Numerical experiments are further conducted to demonstrate the important implications of the proposed optimisation model to production planning in industrial practices. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

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