Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item OPTIMIZATION UNDER STOCHASTIC ENVIRONMENT(2020) Li, Yunchuan; Fu, Michael C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Stochastic optimization (SO) is extensively studied in various fields, such as control engineering, operations research, and computer science. It has found wide applications ranging from path planning (civil engineering) and tool-life testing (industrial engineering) to Go-playing artificial intelligence (computer science). However, SO is usually a hard problem primarily because of the added complexity from random variables. The objective of this research is to investigate three types of SO problems: single-stage SO, multi-stage SO and fast real-time parameter estimation under stochastic environment.\par We first study the single-stage optimization problem. We propose Direct Gradient Augmented Response Surface Methodology (DiGARSM), a new sequential first-order method for optimizing a stochastic function. In this approach, gradients of the objective function with respect to the desired parameters are utilized in addition to response measurements. We intend to establish convergence of the proposed method, as well as traditional approaches which do not use gradients. We expect an improvement in convergence speed with the added derivative information. \par Second, we analyze a tree search problem with an underlying Markov decision process. Unlike traditional tree search algorithms where the goal is to maximize the cumulative reward in the learning process, the proposed method aims at identifying the best action at the root that achieves the highest reward. A new tree algorithm based on ranking and selection is proposed. The selection policy at each node aims at maximizing the probability of correctly selecting the best action. \par The third topic is motivated by problems arising in neuroscience, specifically, a Maximum Likelihood (ML) parameter estimation of linear models with noise-corrupted observations. We developed an optimization algorithm designed for non-convex, linear state-space model parameter estimation. The ML estimation is carried out by the Expectation-Maximization algorithm, which iteratively updates parameter estimates based on the previous estimates. Since the likelihood surface is in general non-convex, a model-based global optimization method called Model Reference Adaptive Search (MRAS) is applied.Item Planning for Integration of Wind Power Capacity in Power Generation Using Stochastic Optimization(2013) Aliari Kardehdeh, Yashar; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The demand for energy is constantly rising in the world while most of the conventional sources of energy are getting more scarce and expensive. Additionally, environmental issues such as dealing with excessive greenhouse gas emissions (especially CO2) impose further constraints on energy industry all over the globe. Therefore, there is an increasing need for the energy sector to raise the share of clean and renewable sources of energy in power generation. Wind power has specifically attracted large scale investment in recent years since it is ample, widely distributed and has minimal environmental impact. Wind flow and consequently wind-generated power have a stochastic nature. Therefore, wind power should be used in combination with more reliable and fuel-based power generation methods. As a result, it is important to investigate how much capacity from each source of energy should be installed in order to meet electricity demand at the desired reliability level while considering cost and environmental implications. For this purpose, a probabilistic optimization model is proposed where demand and wind power generation are both assumed stochastic. The stochastic model uses a combination of recourse and chance-constrained approaches and is capable of assigning optimal production levels for different sources of energy while considering the possibility of importation, exportation and storage of electricity in the network.Item Design and Analysis of Vehicle Sharing Programs: A Systems Approach(2010) Nair, Rahul; Miller-Hooks, Elise D; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transit, touted as a solution to urban mobility problems, cannot match the addictive flexibility of the automobile. 86% of all trips in the U.S. are in personal vehicles. A more recent approach to reduce automobile dependence is through the use of Vehicle Sharing Programs (VSPs). A VSP involves a fleet of vehicles located strategically at stations across the transportation network. In its most flexible form, users are free to check out vehicles at any station and return the vehicle at a station close to their destination. Vehicle fleets are comprised of bicycles, cars or electric vehicles. Such systems offer innovative solutions to the larger mobility problem and can have positive impacts on the transportation system as a whole by reducing urban congestion. This dissertation employs a network modeling framework to quantitatively design and operate VSPs. At the strategic level, the problem of determining the optimal VSP configuration is studied. A bilevel optimization model and associated solution methods are developed and implemented for a large-scale case study in Washington D.C. The model explicitly considers the intermodalism, and views the VSP as a `last-mile' connection of an existing transit network. At the operational level, by transferring control of vehicles to the user for improved system flexibility, exceptional logistical challenges are placed on operators who must ensure adequate vehicle stock (and parking slots) at each station to service all demand. Since demand in the short-term can be asymmetric (flow from one station to another is seldom equal to flow in the opposing direction), service providers need to redistribute vehicles to correct this imbalance. A chance-constrained program is developed that generates least-cost redistribution plans such that most demand in the near future is met. Since the program has a non-convex feasible region, two methods for its solution are developed. The model is applied to a real-world car-sharing system in Singapore where the value of accounting for inherent stochasticities is demonstrated. The framework is used to characterize the efficiency of Velib, a large-scale bicycle sharing system in Paris, France.