Halide perovskite materials have emerged in the past few years as promising materials in the absorption layer of photovoltaic cells and new emissive materials for use in light emitting diodes (LEDs). This is due to their rapidly increasing efficiencies and brightness. In photovoltaic applications they show promise to lower cost and improve efficiency of photovoltaic cells. Their low temperature processability also may lead to interesting new applications in existing solar cell technologies. In LED applications, they exhibit other desirable properties such as color tunability, simple device structures, and facile processability. However, a common problem that is observed in perovskite thin films is a hysteresis in their I-V characteristics, and short device lifetimes. It is hypothesized this is due to ion migration within the crystal and along the grain boundaries between crystals. This thesis addresses this issue by exploring methods to restrict ionic motion. One highly promising method was controlling nucleation to reduce the grain boundary density in the perovskite thin films. A deterministic nucleation process was developed using standard lithography techniques to prepattern a substrate followed by solution processing of the halide perovskite layer. It was found the grain size, grain boundary density, and final crystal shape could be well controlled using this process. In addition, it was found the hysteresis behavior was well controlled, and the stability of the final film was increased due to lower grain boundary density. In addition, further methods to restrict ionic motion were explored using Ruddlesden-Popper perovskites that form a quasi 2D structure. These perovskites were examined and characterized due to their ability to restrict ionic motion within the perovskite crystal. These perovskites also allowed for further flexibility in tuning device electrical and optical properties and offered greater stability compared to their 3D counterparts. / A Dissertation submitted to the Program in Materials Science and Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Fall Semester 2018. / November 5, 2018. / 2D Materials, Grain Boundaries, Halide Perovskites, Optoelectronics / Includes bibliographical references. / Zhibin Yu, Professor Directing Dissertation; John Telotte, University Representative; Kenneth Hanson, Committee Member; Zhiyong Richard Liang, Committee Member; Yan-Yan Hu, Committee Member.

<p> Supply chains are vulnerable to disruptions at any stage of the distribution system. These disruptions can be caused by natural disasters, production problems, or labor defects. The consequences of these disruptions may result in significant economic losses or even human deaths. Therefore, it is important to consider any disruption as an important factor in strategic supply chain design. Consequently, the primary outputs of this dissertation include insights for designing robust supply chains that are neither significantly nor adversely impacted by disruptions.</p><p> The impact of correlated supplier failures is examined and how this problem can be modeled as a variant of a facility location problem is described. Two main problems are defined, the first being the design of a robust supply chain, and the second being the optimization of operational inspection schedules to maintain the quality of an already established supply chain. In this regard, both strategic and operational decisions are considered in the model and (1) a two-stage stochastic programming model; (2) a multi-objective stochastic programming model; and (3) a dynamic programming model are developed to explore the tradeoffs between cost and risk.</p><p> Three methods are developed to identify optimal and robust solutions: an integer L-shaped method; a hybrid genetic algorithm using Data Envelopment Analysis; and an approximate dynamic programming method. Several sensitivity analyses are performed on the model to see how the model output would be affected by uncertainty.</p><p> The findings from this dissertation will be able to help both practitioners designing supply chains, as well as policy makers who need to understand the impact of different disruption mitigation strategies on cost and risk in the supply chain.</p><p>

Evolutionary Algorithms (EAs) as appropriate tools to optimize multi-objective problems have been applied to optimize construction projects in the last two decades. However, studies on improving the convergence ratio and processing time in the most applied algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in construction engineering and management domains remain poorly understood. Furthermore, hybrid algorithms such as Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) and Shuffled Frog Leaping Algorithm (SFLA) have been presented in computational optimization and water resource management domains during recent years to prevent pitfalls of the aforementioned algorithms. In this dissertation, I present three studies on hybrid algorithms to show that our proposed hybrid approaches are superior than existing optimization algorithms in finding better project schedule solutions with less total project cost, shorter total project duration, and less total resources allocation moments. In the first, I present a HGAPSO approach to solve complex, TCRO problems in construction project planning. Our proposed approach uses the fuzzy set theory to characterize uncertainty about the input data (i.e., time, cost, and resources required to perform an activity). In the second, I present the SFLA algorithm to solve TCRO problems using splitting allowed during activities execution. The third study involves the evaluation of the inflation impact on resources unit price during execution of construction projects. This research presents the comprehensive TCRO model by comparing two hybrid algorithms, HGAPSO and SFLA, with the three most capable algorithms -- GA, PSO and ACO -- in six different examples in terms of the structure of projects, construction assumptions and kinds of Time-Cost functions. Each of the three studies helps overcome parts of EAs problems and contributes to obtaining optimal project schedule solutions of total project duration, total project cost and total resources allocation moments of construction projects in the planning stage. The findings have significant implications in improving the scheduling of construction projects.

In this thesis, we study price optimization and competition of multiple differentiated substitutable products under the general Nested Logit model and also consider the designing and pricing of new service products, e.g., flexible warranty and refundable warranty, under customers' strategic claim behavior. Chapter 2 considers firms that sell multiple differentiated substitutable products and customers whose purchase behavior follows the Nested Logit model, of which the Multinomial Logit model is a special case. In the Nested Logit model, customers make product selection decision sequentially: they first select a class or a nest of products and subsequently choose a product within the selected class. We consider the general Nested Logit model with product-differentiated price coefficients and general nest-heterogenous degrees. We show that the adjusted markup, which is defined as price minus cost minus the reciprocal of the price coefficient, is constant across all the products in each nest. When optimizing multiple nests of products, the adjusted nested markup is also constant within a nest. By using this result, the multi-product optimization problem can be reduced to a single-dimensional problem in a bounded interval, which is easy to solve. We also use this result to simplify the oligopolistic price competition and characterize the Nash equilibrium. Furthermore, we investigate its application to dynamic pricing and revenue management. In Chapter 3, we investigate the flexible monthly warranty, which offers flexibility to customers and allow them to cancel it at anytime without any penalty. Frequent technological innovations and price declines severely affect sales of extended warranties as product replacement upon failure becomes an increasingly attractive alternative. To increase sales and profitability, we propose offering flexible-duration extended warranties. These warranties can appeal to customers who are uncertain about how long they will keep the product as well as to customers who are uncertain about the product's reliability. Flexibility may be added to existing services in the form of monthly-billing with month-by-month commitments, or by making existing warranties easier to cancel, with pro-rated refunds. This thesis studies flexible warranties from the perspectives of both the customer and the provider. We present a model of the customer's optimal coverage decisions under the objective of minimizing expected support costs over a random planning horizon. We show that under some mild conditions the customer's optimal coverage policy has a threshold structure. We also show through an analytical study and through numerical examples how flexible warranties can result in higher profits and higher attach rates. Chapter 4 examines the designing and pricing of residual value warranty that refunds customers at the end of warranty period based on customers' claim history. Traditional extended warranties for IT products do not differentiate customers according to their usage rates or operating environment. These warranties are priced to cover the costs of high-usage customers who tend to experience more failures and are therefore more costly to support. This makes traditional warranties economically unattractive to low-usage customers. In this chapter, we introduce, design and price residual value warranties. These warranties refund a part of the upfront price to customers who have zero or few claims according to a pre-determined refund schedule. By design, the net cost of these warranties is lower for light users than for heavy users. As a result, a residual value warranty can enable the provider to price-discriminate based on usage rates or operating conditions without the need to monitor individual customers' usage. Theoretic results and numerical experiments demonstrate how residual value warranties can appeal to a broader range of customers and significantly increase the provider's profits.

Managing the consumption of non-renewable and/or limited resources has become an important issue in many different settings. In this dissertation we explore the topic of resource cost aware scheduling. Unlike the purely scheduling problems, in the resource cost aware setting we are not only interested in a scheduling performance metric, but also the cost of the resources consumed to achieve a certain performance level. There are several ways in which the cost of non-renewal resources can be added into a scheduling problem. Throughout this dissertation we will focus in the case where the resource consumption cost is added, as part of the objective, to a scheduling performance metric such as weighted completion time and weighted tardiness among others. In our work we make several contributions to the problem of scheduling with non-renewable resources. For the specific setting in which only energy consumption is the important resource, our contributions are the following. We introduce a model that extends the previous energy cost models by allowing more general cost functions that can be job-dependent. We further generalize the problem by allowing arbitrary precedence constraints and release dates. We give approximation algorithms for minimizing an objective that is a combination of a scheduling metric, namely total weighted completion time and total weighted tardiness, and the total energy consumption cost. Our approximation algorithm is based on an interval-and-speed-indexed IP formulation. We solve the linear relaxation of this IP and we use this solution to compute a schedule. We show that these algorithms have small constant approximation ratios. Through experimental analysis we show that the empirical approximation ratios are much better than the theoretical ones and that in fact the solutions are close to optimal. We also show empirically that the algorithm can be used in additional settings not covered by the theoretical results, such as using flow time or an online setting, with good approximation and competitiveness ratios.

<p>This praxis develops a simulation-based approach to analyzing the overall reliability of complex systems with high degrees of redundancy, time varying event rates, and the potential for common cause failures. This approach is compared to traditional analytic approaches, and is shown to have some advantages, primarily by avoiding some of the simplifying assumptions used in those approaches. </p><p> Several canonical problems are solved using both traditional and simulation-based approaches to elucidate the method, and the method is then applied to more complex problems for which exact analytic solutions are not available. The method is shown to be flexible to both traditional industrial plant reliability problems and to a new class of problems involving the reliability of swarming unmanned vehicles, where there is a high degree of parallelism and dynamic formation of common cause groups. </p><p> The penultimate chapter examines the impact of common cause failures on the reliability of a swarm of unmanned vehicles performing a search mission, and develops a simulation-based approach to modeling the reliability of swarms in the presence of both independent (single vehicle) and common cause (multiple vehicle) failures. The modeling approach is exercised on a sample problem to illustrate how it can be used as part of a system design or search-planning tool for swarming unmanned vehicles. The simulation provides insight on the impact of design decisions that influence overall system reliability; it also provides metrics of success in a search scenario as a function of user-selectable parameters. </p>

The performance of the electric power system determines the cost-effective and reliable energy supply to maintain operations in a city. Electric power system performance improvement is important for utility companies in different aspects from maintenance and reliability to the environment. In a modern city, new monitoring devices are deployed to collect data in the electric power system and other city systems such as transportation. The heterogeneous data collected by new monitoring devices reveal the multi-community interactions in the electric power system and also reveal the interdependencies between different city systems such as electric power system and transportation system. This dissertation research studied the development of data fusion and multi-task learning algorithms in improving short-term load forecasting, fault detection, and rare faulty event detection by leveraging heterogeneous and multi-community data. The theoretical contribution of this study lies in the method selection and comparison for fusing transportation and electricity consumption data, and new methods of capturing between-community relatedness in guiding the knowledge transfer for the learning of Bayesian spatiotemporal Gaussian Process model, fault classification, and semi-supervised learning so that the performance of these algorithms are not limited by the specificity in the dataset and can reduce overfitting issues. The first study aims to forecast the electric load consumption and traffic counts accurately which benefits from the data fusion techniques in order to fill the lack of sufficient data. Accurate forecasting is mostly dependent on sufficient and reliable data. Traditional data collection methods may be necessary but not sufficient due to their limited coverage and expensive cost of implementation and maintenance. The advances in sensor networks and recent technological developments emerge a new opportunity. Specifically, data fusion tools can be used for improving the limited resolution in the data due to limitations on time frame, cost, accuracy, and reliability. In this study, a Bayesian spatiotemporal Gaussian Process model is proposed which employs the most informative spatiotemporal interdependency among its system, and covariates from other city systems. Results obtained from real-world data from the City of Tallahassee in Florida show that the multi-network data fusion framework improves the accuracy of load forecasting, and the proposed model outperforms all the existing methods. The second study is conducted for short-term electricity load forecasting for a residential community in a city which suffers from low-resolution data. Historically, extensive research has been conducted to improve the load forecasting accuracy using single-task machine learning methods, which rely on the information from one single data source. Such methods have limitations with low-resolution data from meters. Fusing the electricity consumption data from multiple communities can improve forecasting accuracy. Recently, an emerging family of machine learning algorithms, multi-task learning (MTL), have been developed and can be utilized for short-term load forecasting. However, appropriate modeling of the relatedness to enable the between-community knowledge transfer remains a challenge. This research proposes an improved MTL algorithm for a Bayesian spatiotemporal Gaussian process model (BSGP) to characterize the relatedness among the different communities in a city. It hypothesizes on the similar impacts of environmental and traffic conditions on electricity consumption in improving the accuracy of short-term electricity load forecasting. Furthermore, this study proposes a low ranked dirty model along with an iterative algorithm to improve the learning of model parameters under an MTL framework. This study used real-world data from two residential communities to demonstrate the proposed method through comparison with state-of-the-art methods. The third study investigates the fault (type) detection in power distribution systems by using the Distribution Phasor Measurement Unit (D-PMU) data. Historically, Traveling-wave and impedance-based methods are among the most notable fault detection techniques. The disadvantage of the impedance methods is that they rely on the knowledge of the network components characteristics. Although Traveling-wave methods have shown to be accurate, they require high-frequency measurements for reliable performance. Such high-resolution measurement data is expensive and may not be available all the times. More recently, D-PMU devices are used to observe better, record, and provide high-resolution voltage and current phasor measurements. In this study, a Multi-task Logistic Low-Ranked Dirty Model (MT-LLRDM) for fault detection is proposed to improve the accuracy by utilizing the similarities in the fault data streams among multiple locations across a power distribution network. The captured similarities supplement the information to the task of fault detection at a location of interest, creating a multi-task learning framework and thereby improving the learning accuracy. The algorithm is validated with real-time D-PMU streams from a hardware-in-the-loop testbed that emulates real field communication and monitoring conditions in distribution networks. Finally, a study is conducted for the fault (type) detection in power distribution systems when data suffers from the lack of labeled data. Supervised multi-task learning methods have limitations when there are a lot of missing data in the target domain especially records on fault data are lacking label. Labeled fault data can be very limited in the target community since fault data labeling is very time-consuming. Therefore, in this study, a multi-task semi-supervised learning method is proposed to simultaneously explore the latent structure in the unlabeled data to learn the labels and leverage the data from multiple locations in the power systems to improve the fault detection. / A Dissertation submitted to the Department of Industrial and Manufacturing Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2019. / April 15, 2019. / Data Fusion, Electric Power Systems, Multi-task Learning / Includes bibliographical references. / Hui Wang, Professor Directing Dissertation; Ren Moses, University Representative; Eren Erman Ozguven, Committee Member; Chiwoo Park, Committee Member; Omer Arda Vanli, Committee Member.

This work comprises several projects that involve optimization of physical systems. By a physical system we understand an object or a process that is governed by physical, mechanical, chemical, biological, etc., laws. Such objects and the related optimization problems are relatively rarely considered in operations research literature, where the traditional subjects of optimization methods are represented by schedules, assignments and allocations, sequences, and queues. The corresponding operations research and management sciences models result in optimization problems of relatively simple structure (for example, linear or quadratic optimization models), but whose difficulty comes from very large number (from hundreds to millions) of optimization variables and constraints. In contrast, in many optimization problems that arise in mechanical engineering, chemical engineering, biomedical engineering, the number of variables or constraints in relatively small (typically, in the range of dozens), but the objective function and constraints have very complex, nonlinear and nonconvex analytical form. In many problems, the analytical expressions for objective function and constraints may not be available, or are obtained as solutions of governing equations (e.g., PDE-onstrained optimization problems), or as results of external simulation runs (black-box optimization). In this dissertation we consider problems of classification of biomedical data, construction of optimal bounds on elastic tensor of composite materials, multiobjective (multi-property) optimization via connection to stochastic orderings, and black-box combinatorial optimization of crystal structures of organic molecules.

The need for effective energy harvesting from renewable resources becomes increasingly important, especially in the light of the inevitable depletion of the fossil fuel energy sources. Among renewable energy sources, wind energy represents one of the most attractive alternatives. In this thesis, we construct several stochastic optimization models, including the traditional risk-neutral expectation based model, and risk-averse models based on linear and nonlinear coherent measures of risk, to study the strategic planning and operation of futuristic power grids where the loads are served from renewable energy sources (wind farms) through High Voltage Direct Current lines. Exact solutions algorithms that employ Benders decomposition and polyhedral approximations of nonlinear constraints have been proposed for the formulated linear and nonlinear mixed-integer optimization problems. The conducted numerical experiments illustrate the efficiency of the developed algorithms, as well as effectiveness of risk-averse models in reducing the power grid's exposure to power shortage risks when the energy is produced from renewable sources. We further extend the risk-averse models to demonstrate how energy storage devices may impact the risk profile of power shortages in the renewable energy power grid. Additionally, we consider convex relaxations of optimal power flow problem over radial networks, that allow for solving mixed-integer optimization problems in traditional alternating current distribution networks. Exactness of a specific second-order cone programming relaxation has been discussed. We finally propose an “ extended” optimal power flow problem and prove its second-order cone programming relaxation to be exact theoretically and empirically.

Advanced composites which offer robust mechanical properties are being increasingly used for structural applications in the aerospace, marine, defense and transportation industries. However, the anisotropic nature of composite materials leaves it susceptible to problematic failure; the development of means for detecting failure is imperative. As design and functionality requirements of engineering structures such as spacecraft, aircraft, naval vessels, buildings, dams, bridges and ground-based vehicles become more complex; structural health monitoring (SHM) and damage assessment is becoming more rigorous. Though structures involved have regular costly inspections, the damage associated with composites in SHM systems can lead to catastrophic and expensive failures. Industry and research have no single technique used on its own to provide reliable results. Integrating several nondestructive evaluation (NDE) techniques could provide a solution for real-time health monitoring. Such studies, utilizing acoustic emission (AE), A-scans, C-scans, and laser shearography have reported considerable success. Nevertheless, damage detection has to be reliable and cost effective. The answer may lie with the development of SHM systems by the use of triboluminescent crystals, as well as optical fibers embedded in the composite matrix. These crystals react to straining or fracture by emitting light of varied luminous intensity. Thus, a fiber-reinforced plastic (FRP) laminate doped with Triboluminescent (TL) or Mechanoluminescent (ML) crystals, acting as health sensors to its host material, will give an indication of crack initiation well ahead of catastrophic failure(s). The development of an in-situ health monitoring system for safety critical structures is a viable route through 'Triboluminescence'. Assessing the viability of a proposed structural sensor system requires cross-linking between key areas in science and engineering. Initial testing has shown that light can propagate through doped resins alone, as well as doped FRP laminates. The luminous intensities relation to impact velocity adds credence to a monitoring system that can characterize impact activity. However, Triboluminescent crystals have high material density. In response, a two-dimensional rotational mold was built to counteract massive settling under normal vacuum molding processes. Micro-structural evaluations using scanning electron microscopy (SEM) and EDAX imaging have aided in demystifying particulate dispersion of TL fillers through use of image processing. / A Thesis submitted to the Department of Industrial Engineering in partial fulfillment of the Requirements for the degree of Master of Science. / Degree Awarded: Spring Semester, 2007. / Date of Defense: March 26, 2007. / Triboluminescence, Composites, Dispersion / Includes bibliographical references. / Okenwa Okoli, Professor Directing Thesis; Zhiyong Liang, Committee Member; James Simpson, Committee Member.