Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item Working in Reverse: Advancing Inverse Optimization in the Fields of Equilibrium and Infrastructure Modeling(2022) Allen, Stephanie Ann; Gabriel, Steven A; Dickerson, John P; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transportation and infrastructure modeling allows us to pursue societal aims such as improved disaster management, traffic flow, and water allocation. Equilibrium programming enables us to represent the entities involved in these applications such that we can learn more about their dynamics. These entities include transportation users and market players. However, determining the parameters in these models can be a difficult task because the entities involved in these equilibrium processes may not be able to articulate or to disclose the parameterizations that motivate them. The field of inverse optimization (IO) offers a potential solution to this problem by taking observed equilibria to these systems and using them to parameterize equilibrium models. In this dissertation, we explore the use of inverse optimization to parameterize multiple new or understudied subclasses of equilibrium problems as well as expand inverse optimization's application to new infrastructure domains. In the first project of our dissertation, our contribution to the literature is to propose that IO can be used to parameterize cost functions in multi-stage stochastic programs for disaster management and can be used in disaster support systems. We demonstrate in most of our experiments that using IO to obtain the hidden cost parameters for travel on a road network changes the protection decisions made on that road network when compared to utilizing the mean of the parameter range for the hidden parameters (also referred to as ``uniform cost''). The protection decisions made under the IO cost parameterizations versus the true cost parameterizations are similar for most of the experiments, thus lending credibility to the IO parameterizations. In the second project of our dissertation, we extend a well-known framework in the IO community to the case of jointly convex generalized Nash equilibrium problems (GNEPs). We demonstrate the utility of this framework in a multi-player transportation game in which we vary the number of players, the capacity level, and the network topology in the experiments as well as run experiments assuming the same costs among players and different costs among players. Our promising results provide evidence that our work could be used to regulate traffic flow toward aims such as reduction of emissions. In the final project of our dissertation, we explore the general parameterization of the constant vector in linear complementarity problems (LCPs), which are mathematical expressions that can represent optimization, game theory, and market models (Gabriel et al., 2012). Unlike the limited previous work on inverse optimization for LCPs, we characterize theoretical considerations regarding the inverse optimization problem for LCPs, prove that a previously proposed IO solution model can be dramatically simplified, and handle the case of multiple solution data points for the IO LCP problem. Additionally, we use our knowledge regarding LCPs and IO in a water market allocation case study, which is an application not previously explored in the IO literature, and we find that charging an additional tax on the upstream players enables the market to reach a system optimal. In sum, this dissertation contributes to the inverse optimization literature by expanding its reach in the equilibrium problem domain and by reaching new infrastructure applications.Item Optimization of Signal Routing in Disruption-Tolerant Networks(2021) Singam, Caitlyn; Ephremides, Anthony; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Communication networks are prone to disruption due to inherent uncertainties such as environmental conditions, system outages, and other factors. However, current state-of-the-art communication protocols are not yet optimized for communication in highly disruption-prone environments, such as deep space, where the risk of such uncertainties is not negligible. This work involves the development of a novel protocol for disruption-tolerant communication across space-based networks that avoids idealized assumptions and is consistent with system limitations. The proposed solution is grounded in an approach to information as a time-based commodity, and on reframing the problem of efficient signal routing as a problem of value optimization. The efficacy of the novel protocol was evaluated via a custom Monte Carlo simulation against other state-of-the-art protocols in terms of maintaining both data integrity and transmission speed, and was found to provide a consistent advantage across both metrics of interest.Item MULTI-AGENT UNMANNED UNDERWATER VEHICLE VALIDATION VIA ROLLING-HORIZON ROBUST GAMES(2019) Quigley, Kevin J; Gabriel, Steven A.; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Autonomy in unmanned underwater vehicle (UUV) navigation is critical for most applications due to inability of human operators to control, monitor or intervene in underwater environments. To ensure safe autonomous navigation, verification and validation (V&V) procedures are needed for various applications. This thesis proposes a game theory-based benchmark validation technique for trajectory optimization for non-cooperative UUVs. A quadratically constrained nonlinear program formulation is presented, and a "perfect-information reality" validation framework is derived by finding a Nash equilibrium to various two-player pursuit-evasion games (PEG). A Karush-Kuhn-Tucker (KKT) point to such a game represents a best-case local optimum, given perfect information available to non-cooperative agents. Rolling-horizon foresight with robust obstacles are incorporated to demonstrate incomplete information and stochastic environmental conditions. A MATLAB-GAMS interface is developed to model the rolling-horizon game, and is solved via a mixed complementarity problem (MCP), and illustrative examples show how equilibrium trajectories can serve as benchmarks for more practical real-time path planners.Item Approximation Algorithms for Geometric Clustering and Touring Problems(2018) Bercea, Ioana Oriana; Khuller, Samir; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Clustering and touring are two fundamental topics in optimization that have been studied extensively and have ``launched a thousand ships''. In this thesis, we study variants of these problems for Euclidean instances, in which clusters often correspond to sensors that are required to cover, measure or localize targets and tours need to visit locations for the purpose of item delivery or data collection. In the first part of the thesis, we focus on the task of sensor placement for environments in which localization is a necessity and in which its quality depends on the relative angle between the target and the pair of sensors observing it. We formulate a new coverage constraint that bounds this angle and consider the problem of placing a small number of sensors that satisfy it in addition to classical ones such as proximity and line-of-sight visibility. We present a general framework that chooses a small number of sensors and approximates the coverage constraint to arbitrary precision. In the second part of the thesis, we consider the task of collecting data from a set of sensors by getting close to them. This corresponds to a well-known generalization of the Traveling Salesman Problem (TSP) called TSP with Neighborhoods, in which we want to compute a shortest tour that visits at least one point from each unit disk centered at a sensor. One approach is based on an observation that relates the optimal solution with the optimal TSP on the sensors. We show that the associated bound can be improved unless we are in certain exceptional circumstances for which we can get better algorithms. Finally, we discuss Maximum Scatter TSP, which asks for a tour that maximizes the length of the shortest edge. While the Euclidean version admits an efficient approximation scheme and the problem is known to be NP-hard in three dimensions or higher, the question of getting a polynomial time algorithm for two dimensions remains open. To this end, we develop a general technique for the case of points concentrated around the boundary of a circle that we believe can be extended to more general cases.Item HYBRID ROUTING MODELS UTILIZING TRUCKS OR SHIPS TO LAUNCH DRONES(2018) Poikonen, Stefan Allan; Golden, Bruce L; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Technological advances for unmanned aerial vehicles, commonly referred to as drones, have opened the door to a number of new and interesting applications in areas including military, healthcare, communications, cinematography, emergency response, and logistics. However, limitations due to battery capacity, maximum take-off weight, finite range of wireless communications, and legal regulations have restricted the effective operational range of drones in many practical applications. Several hybrid operational models involving one or more drones launching from a larger vehicle, which may be a ship, truck, or airplane, have emerged to help mitigate these range limitations. In particular, the drones utilize the larger vehicle as both a mobile depot and a recharging or refueling platform. In this dissertation, we describe routing models that leverage the tandem of one or more drones with a larger vehicle. In these models, there is generally a set of targets that should be visited in an efficient (usually time-minimizing) manner. By using multiple vehicles, these targets may be visited in parallel thereby reducing the total time to visit all targets. The vehicle routing problem with drones (VRPD) and traveling salesman problem with a drone (TSP-D) consider hybrid truck-and-drone models of delivery, where the goal is to minimize the time required to deliver a set of packages to their respective customers and return the truck(s) and drone(s) to the origin depot. In both problems, the drone can carry one homogeneous package at a time. Theoretical analysis, exact solution methods, heuristic solution methods, and computational results are presented. In the mothership and drone routing problem (MDRP), we consider the case where the larger launch vehicle is free to move in Euclidean space (the open seas) and launch a drone to visit one target location at a time, before returning to the ship to pick up new cargo or refuel. The mothership and high capacity drone routing problem (MDRP-HC) is a generalization of the mothership and drone routing problem, which allows the drone to visit multiple targets consecutively before returning to the ship. MDRP and MDRP-HC contain elements of both combinatorial optimization and continuous optimization. In the multi-visit drone routing problem (MVDRP), a drone can visit multiple targets consecutively before returning to the truck, subject to energy constraints that take into account the weight of packages carried by the drone.Item A Model-Based Systems Engineering Simulation: Analysis and Design of Hospital Bed Maintenance in Critical Health Care Systems(2018) Dávila Andino, Arturo; Fu, Michael C.; Wood, Kenneth E.; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis summarizes the results of various methodologies integrated to solve a staffing problem when cleaning and maintaining hospital beds. First, a simplified systems engineering design model was developed to translate the need for reducing the total turnaround time of maintaining hospital beds into a performance requirement of the average time a hospital bed waits for service. The tools that were used were queueing analysis, discrete-event simulation modeling, and optimization via simulation. Finally, this work presents the derived staffing requirements from the pertinent measure of effectiveness, the average waiting time.Item ENERGY HARVESTING MICROGENERATORS FOR BODY SENSOR NETWORKS(2014) Dadfarnia, Mehdi; Baras, John S; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Body sensor networks have the potential to become an asset for personalizing healthcare delivery to patients in need. A key limitation for a successful implementation of body sensor networks comes from the lack of a continuous, reliable power source for the body-mounted sensors. The aim of this thesis is to model and optimize a micro-energy harvesting generator that prolongs the operational lifetime of body sensors and make them more appealing, especially for personalized healthcare purposes. It explores a model that is suitable for harvesting mechanical power generated from human body motions. Adaptive optimization algorithms are used to maximize the amount of power harvested from this model. Practicality considerations discuss the feasibility of optimization and overall effectiveness of implementing the energy harvester model with respect to body sensor power requirements and its operational lifetime.Item Lexical Features for Statistical Machine Translation(2009) Devlin, Jacob; Dorr, Bonnie; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In modern phrasal and hierarchical statistical machine translation systems, two major features model translation: rule translation probabilities and lexical smoothing scores. The rule translation probabilities are computed as maximum likelihood estimates (MLEs) of an entire source (or target) phrase translating to a target (or source) phrase. The lexical smoothing scores are also a likelihood estimate of a source (target) phrase translating to a target (source) phrase, but they are computed using independent word-to-word translation probabilities. Intuitively, it would seem that the lexical smoothing score is a less powerful estimate of translation likelihood due to this independence assumption, but I present the somewhat surprising result that lexical smoothing is far more important to the quality of a state-of-the-art hierarchical SMT system than rule translation probabilities. I posit that this is due to a fundamental data sparsity problem: The average word-to-word translation is seen many more times than the average phrase-to-phrase translation, so the word-to-word translation probabilities (or lexical probabilities) are far better estimated. Motivated by this result, I present a number of novel methods for modifying the lexical probabilities to improve the quality of our MT output. First, I examine two methods of lexical probability biasing, where for each test document, a set of secondary lexical probabilities are extracted and interpolated with the primary lexical probability distribution. Biasing each document with the probabilities extracted from its own first-pass decoding output provides a small but consistent gain of about 0.4 BLEU. Second, I contextualize the lexical probabilities by factoring in additional information such as the previous or next word. The key to the success of this context-dependent lexical smoothing is a backoff model, where our "trust" of a context-dependent probability estimation is directly proportional to how many times it was seen in the training. In this way, I avoid the estimation problem seen in translation rules, where the amount of context is high but the probability estimation is inaccurate. When using the surrounding words as context, this feature provides a gain of about 0.6 BLEU on Arabic and Chinese. Finally, I describe several types of discriminatively trained lexical features, along with a new optimization procedure called Expected-BLEU optimization. This new optimization procedure is able to robustly estimate weights for thousands of decoding features, which can in effect discriminatively optimize a set of lexical probabilities to maximize BLEU. I also describe two other discriminative feature types, one of which is the part-of-speech analogue to lexical probabilities, and the other of which estimates training corpus weights based on lexical translations. The discriminative features produce a gain of 0.8 BLEU on Arabic and 0.4 BLEU on Chinese.