Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

First-order deterministic and stochastic optimization algorithms have gained significant importance in the past two decades primarily due to applications in data science and machine learning. In numerous recent problems, however, obtaining the exact gradient (in deterministic settings) or an unbiased gradient estimator (in stochastic settings) is computationally challenging. A naive implementation of the classical algorithms for such problems results in sub-optimal performance and unsatisfactory results. To address this gap, this research aims to investigate the properties of these problems, design new optimization algorithms, and investigate their theoretical convergence guarantees. Along this path, the dissertation makes three main contributions: 1) algorithmic development and in-depth analyses for a specific problem, namely stochastic composition optimization; 2) exploration of three biased stochastic approximation algorithms for the general setup and their theoretical analysis in the nonconvex setting; 3) investigation of accelerated gradient descent method for problems with inexact gradient oracles in convex setups and derivation of a new upper bound for the accumulated error. The first work focuses on the stochastic composition optimization problem. It explores scenarios where either the inner or outer function lacks Lipschitz continuity of their gradients. To generalize the assumption of Lipschitz continuity of the gradients, the notion of relative smoothness is introduced. The properties of composition gradients for three non-trivial combinations are examined, leading to a discussion of their corresponding smoothness properties. For each type of composition problem, first-order methods are proposed and their convergence analyses are conducted. Furthermore, the sample complexities for these proposed algorithms are established. The theoretical findings are then validated through experimental results. The subsequent research presents a unified fr (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

Energy storage is widely used to respond to the uncertain balance of electricity supply and demand and prepare for the contingency. Among many purposes of energy storage, this dissertation will focus on arbitrage trade, peak load shift, and frequency regulation. For the first part, a two-stage stochastic programming model is introduced to schedule energy storage devices and maximize arbitrage profits for the storage operator. In addition, the model considers adjustments depending on the uncertain price of the real-time electricity market when the decision in the day-ahead market is made. Then, value of stochastic solution is computed to see effect of the stochastic programming. Furthermore, several interesting cases are observed and illustrated, such as simultaneous charging and discharging. These are considered as an sub-optimal solution in general, but this occurs in specific conditions. Second, when storage is used for peak load shift, it improves resource adequacy of the power systems by contribution of the power from energy storage. In this chapter, a non-performance penalty is imposed to ensure that energy storage operators reserve energy for such shortages. A stochastic dynamic programming model is used to obtain optimal decision policy for the storage device. Using this model, case studies are conducted for the two different systems. System load of these systems are peaked in the summer and winter, so these are analyzed and compared. In the third part, energy storage capacity value and expected profits are estimated when it provides energy, capacity, and frequency regulation services. To estimate capacity value, three steps approach is adopted. First, discretized stochastic dynamic programming is used to obtain decisions policies for the discretized states. These decision policies are used to get actual decisions by solving mixed-integer optimization in a rolling-horizon fashion. Then, capacity value of energy storage is estimated using simulation. A case (open full item for complete abstract)

PhD, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

The path planning and power management of hybrid fuel UAVs under presence of noise-restrictions is studied here. This problem is motivated by two scenarios: i) widespread use of UAVs in congested, urban environment; and ii) Noise-sensitive surveillance missions. In either case, it is envisioned that noise-restrictions are in place in subsets of the environment, such that ground-level noise produced by the UAV at hand must be under a certain intensity. In the case of urban usage, we consider it likely that such restrictions are eventually put in place near residential and business areas. In the case of a hybrid-fuel UAV, where energy sources include a battery-pack and combustion engine, the noise produced by the engine is intense relative to the propeller noise. In this scenario, the path planning and power planning is a coupled problem: given a path, certain power plans are infeasible, and given an energy plan certain paths are infeasible. Thus, the path of the UAV must be found in tandem with the power plan. This results in a novel problem, which we study here. The single-agent problem is studied first within a discrete framework, as is standard for vehicle motion planning. An environment is discretized into a graph, such that nodes represent locations in the configuration space and edges between the nodes are flight legs the UAV travels along between nodes. Edges are parameterized by cost and energy values. The objective is to find a feasible sequence of nodes of lowest cost without violating the power and noise constraints. We develop a fast, exact algorithm to solve this planning problem quickly on graphs of tens of thousands of nodes. The problem is approached in an optimal control framework, with only an initial approach presented in this dissertation. Battery modeling in the context of this problem is also studied briefly. The final piece of work is returning to the discrete problem in the context of multi-agent path finding (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2024, Statistics

Deep learning, neural network, has been penetrating into almost every corner of data analyses. With advantages on computing power and speed, adding more layers in a neural network analysis becomes a common practice to improve the prediction accuracy. However, over depleting information in the training dataset may consequently carry data noises into the learning process of neural network and result in over-fitting errors. Neural Network has been used to predict the future time series data. It had been claimed by several authors that the Neural Network (Recurrent Neural Network) can predict the time series data, although time series models have also been used to predict the future time series data. This dissertation is thus motivated to investigate the prediction performances of neural networks versus the conventional inference method of time series analysis. After introducing basic concepts and theoretical background of neural network and time series prediction in Chapter 1, Chapter 2 analyzes fundamental structure of time series, along with estimation, hypothesis testing, and prediction methods. Chapter 3 discusses details of computing algorithms and procedures in neural network with theoretical adjustment for time series prediction. In conjunction with terminologies and methodologies in the previous chapters, Chapter 4 directly compares the prediction results of neural networks and conventional time series for the squared error function. In terms of methodology assessment, the evaluation criterion plays a critical role. The performance of the existing neural network models for time series predictions has been observed. It has been experimentally observed that the time series predictions by time series models are better compared to the neural network models both computationally and theoretically. The conditions for the better performances of the Time Series Models over the Neural Network Models have been discovered. Theorems have also been pro (open full item for complete abstract)

Undergraduate Honors Program, Malone University, 2024, Honors Thesis

Objectives: University course timetabling is a complex task involving the allocation of instructors, timeslots, and rooms to courses while adhering to various constraints. Our objective is to develop a decision-support system that can streamline and optimize the course timetabling process at Malone University. Methods: We present a novel three-stage binary integer linear programming model designed to address the specific timetabling challenges faced by the Department of Natural Sciences at Malone University. This model is implemented in Excel and solved using OpenSolver, an Excel add-in for linear programming. Results: Utilizing real data from Malone University's Department of Natural Sciences for the Fall 2024 semester, our proposed model demonstrates efficient and effective automation of course timetabling through successful generation of a timetable.

Master of Science in Engineering, Youngstown State University, 2024, Department of Mechanical, Industrial and Manufacturing Engineering

Lithium-ion batteries (LIBs) have been a vital technology since they were introduced to the world in the 1990s. Despite significant advancements in cost-effectiveness and production efficiency, there are still some obstacles that need to be addressed. Significantly, as lithium-ion battery (LIB) technology is increasingly used in the transportation industry to enable electric vehicles, the issue of industrial ethics and environmental sustainability becomes of extreme importance. We are currently developing water-based manufacturing procedures to achieve more environmentally friendly production of lithium-ion batteries. Our research focuses on analyzing the design elements and process dynamics involved in removing solvents from the electrode coatings of these batteries. We demonstrate the impact of substituting N-Methyl-2-pyrrolidone (NMP) with an aqueous solvent, specifically water, in the electrode. To describe the process of cathode drying, we employ a mathematical model at the continuous level. This model accounts for the simultaneous transmission of heat and mass, as well as phase change. The utilization of aqueous processing for electrode material has the potential of cost reduction and environmental effect reduction in existing lithium-ion battery (LIB) manufacturing processes. By substituting costly and hazardous binder solvents like N-methyl-2-pyrrolidone (NMP) with water-based processing, both material expenses and processing and capital equipment costs can be minimized. The optimization model will determine the most efficient factors for the energy consumption of the solvent drying process, which constitutes a significant component of the overall energy consumption in the drying process.

Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

Decision-making problems in real life often involve uncertainties. One way to address such problems is to use stochastic optimization, where quantifying a probability distribution to represent the underlying uncertainty is critical. However, most often, only partial information about the uncertainty is available through a series of historical data and expert knowledge. This limitation becomes particularly significant if the decision maker is risk averse and needs to consider rare but high-impact events, for which the probability distribution cannot be accurately determined even with the available historical data. Distributionally Robust Stochastic Optimization (DRO) is an alternative approach that assumes that the underlying distribution is unknown but instead lies in an ambiguity set of distributions that is consistent with the available data. DRO then tries to optimize the worst-case expectation among all distributions in the ambiguity set. This dissertation focuses on effective scenarios in DROs defined using a finite number of realizations (also called scenarios) of the uncertain parameters. Effective scenarios are the critical scenarios in DRO in the sense that their removal alters the optimal objective function value. Ineffective scenarios, on the other hand, can be removed safely without changing the optimal value. In this dissertation, we investigate both the theoretical and computational aspects of effective scenarios. The first contribution of this dissertation links the effectiveness of a scenario to its worst-case distribution being always positive or uniquely zero under a general ambiguity set with finite support. We then narrow down our focus to DROs with ambiguity sets formed via the Cressie-Read power divergence family (DRO-CR) and the Wasserstein distance (DRO-W). This class of problems constitutes some of the most widely used DROs in the literature. We provide easy-to-check sufficient conditions to identify the effectiveness of scenarios fo (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Health and Rehabilitation Sciences

This dissertation presents the development and evaluation of the Agile Healthcare Performance Index (AHPI), a novel methodology designed to improve quality and measure performance within healthcare settings. It offers a framework designed to capture the complexities of healthcare delivery. Chapter 3 introduces the AHPI, emphasizing its significance in enhancing resource allocation and operational decision-making through an analysis of synthetic data across hospital service lines. The results underscore the adaptability and temporal sensitivity compared to static, unweighted indices, highlighting the potential to refine healthcare performance measurement. Chapter 4 extends the application of the AHPI to quality improvement (QI) initiatives, hypothesizing its effectiveness in aligning healthcare decision-making processes with the complex nature of care delivery. A simulation-based case study illustrates the alignment of the AHPI with the Cynefin framework's domains, demonstrating its strategic utility in navigating the dynamic challenges of healthcare. Chapter 5 focuses on the practical application of the AHPI in evaluating hip fracture care among the elderly, utilizing data from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP). The findings reveal the ability of the AHPI to accurately reflect variations in surgical outcomes, validating its role as a dynamic tool for quality improvement and policymaking across healthcare settings. Together, these studies advocate for the AHPI as a groundbreaking approach to healthcare performance assessment and QI. By integrating multidimensional metrics and a data-driven methodology, using the AHPI can provide a robust solution for enhancing care quality and operational efficiency, paving the way for a more adaptable and effective healthcare system.

Doctor of Philosophy (PhD), Ohio University, 2024, Mechanical and Systems Engineering (Engineering and Technology)

The world is currently experiencing a shortage of semiconductor chips. This shortage is affecting different industries that rely on electronic components that involve semiconductor chips to manufacture their products. Due to the shortage of chips, manufacturers are unable to complete the final assembly of their products, resulting in a delay in delivering the finished products to their customers. To address this issue, the US Congress passed the "Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act of 2022" on 9th August, 2022. This act aims to improve the competitiveness, innovation, and national security of the US. This dissertation focuses on addressing the chip shortage through the reduction of chronic semiconductor manufacturing process quality problems caused by wafer map surface defects. The proposed solution involves detecting mixed-type wafer map surface defect patterns using Ensemble Deformable Convolutional Neural Networks. The framework for defect detection proposed in this dissertation outperforms other machine learning models from literature, such as Conv-Pool-CNN, All-CNN, NIN-CNN, DCNN-v1, and DCNN-v2, in terms of F1-score. The proposed framework uses an industrial wafer map dataset (MixedWM38) from a semiconductor wafer manufacturing process to train the base models for the ensemble method. The results show that the proposed framework accurately identifies multi-pattern defects from the surface of wafer maps. This dissertation will contribute to advancing academic literature for the new field of detecting mixed-type defect patterns from the surface of wafer maps. Defects are indicators of process problems, and preventing quality defects in advance is the best approach to achieving positive yield. The efficient and accurate detection of wafer map mixed-type surface defect patterns is important for addressing chronic manufacturing process quality problems. The proposed framework can be used by semiconductor manufacturer (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Industrial and Systems Engineering

The design of electricity markets is a challenging task. On one hand, it is complicated by the different characteristics of each market design, an example of which is the classification of either a centrally committed market or a self-committed market. Under a centrally committed market, generators submit variable and fixed offers to a market operator (MO) and the market is cleared by minimizing the sum of both types of costs. Under a self-committed market, only variable offers are passed to the MO. On the other hand, it is involved with the dynamics of new technology integration, which includes the rapid development of energy storage and renewable energy. Since issues such as policy equality and market efficiency play a vital role in social welfare, they require thorough study from the research community continuously. This dissertation investigates three issues emerging from electricity market design by applying operations research techniques. The first topic is about the impact of having market operators dispatch energy storage. There is a debate on whether we should authorize market operators to make energy-storage-operations decisions. Some stakeholders argue that having market operators dispatch energy storage is akin to market operators owning and operating generation. As a result, the independence of a market operator may be compromised. It is important we clear up this misconception before appropriate treatment of energy storage can occur. To analyze the market clearing condition, we apply duality theory on a stylized optimalpower- flow problem. The second topic is about the comparison between two market designs, i.e., a centrally-committed model adopted in North America and a self-committed model employed by western Europe. The key difference is in the offer submitted by generating entities as well as how the MO utilizes these offers under the two market designs. Additionally, we allow for strategic behavior of market participant. We form (open full item for complete abstract)

Doctor of Education (EdD), Ohio University, 2023, Educational Administration (Education)

This study explored the Deming System of Profound Knowledge as a method of leadership and management in K-8 education. The study focused on the process of acquiring and understanding Deming's teachings as they related to the principalship and educational leadership. Using autoethnography as methodology, I leverage personal qualitative data and related educational leadership literature to present my personal journey of becoming an educational leader who practices Deming's System of Profound Knowledge as their primary method for leading and managing a school. Upon reflection and analysis, I found W. Edwards Deming's System of Profound Knowledge practical and valuable as a leadership method in today's schools. Though the mastery of Deming's teachings was a long, complex process, it greatly improved my leadership practice. The findings highlight essential knowledge and skills required to understand and practice the System of Profound Knowledge. It connects educational leadership and Deming's method and recommends further research.

Master of Science (MS), Ohio University, 2023, Industrial and Systems Engineering (Engineering and Technology)

Human trafficking (HT) is a form of contemporary slavery that affects individuals in every state of the United States. Despite the existence of government and non-profit rehabilitation services, HT-affected individuals often miss out due to improper resource allocation. To address this issue, we propose an optimization model that efficiently allocates resources to rehabilitate and reintegrate HT-affected individuals where they are most needed. Our strategy uses a Mixed Integer Linear Programming model to optimize the net societal value (NSV) gained from offering support services while considering the three stages of HT-affected people's healing path, including victim, survivor, and thriver. This model determines the optimal type, quantity, and location of services while also integrating HT risk scores that account for the risk of HT in those areas. Our model's efficacy is demonstrated in an Ohio case study, allocating housing, detoxification, and food services across the state's eighty-eight counties and three stages of the healing path of HT-affected individuals. Through Monte Carlo Simulation in the solution approach, uncertain demand is accounted for, leading to improved NSV under such conditions. Moreover, we illustrate the impact of an increased budget, showcasing extended service reach and allocation possibilities. Our work aims to support decision-makers in efficiently allocating resources to rehabilitate and reintegrate HT-affected individuals effectively.

Doctor of Philosophy (PhD), Ohio University, 0, Mechanical and Systems Engineering (Engineering and Technology)

This research presents a novel multi-objective mathematical model for the design of a three-echelon sustainable supply chain network comprising suppliers, assemblers, and customers. The research aims to optimize three sustainability functions, namely economic, environmental, and social aspects. The proposed integrated optimization model addresses four key decision areas: (1) locating assembly plants and determining their manufacturing capacity and line configurations, (2) selecting transportation modes for the delivery of parts from suppliers to assemblers and the final product to customers, (3) supplier selection, and (4) choosing the source of energy from a range of conventional and renewable options. This research investigates the interactions between sustainability objectives by analyzing the results obtained through a Pareto frontier approach. The study aims to enable decision-makers to select their preferred option from a range of scenarios. To showcase the practical application of the proposed optimization model, a case study involving a US truck manufacturer is conducted. The findings of the study reveal the trade-offs that exist among the sustainability criteria, providing decision-makers with a variety of alternatives to align their business strategies accordingly. The proposed problem is a multi-objective mixed-integer non-linear programming model that incorporates chance constraints to account for energy usage uncertainties in the assembly plant. The integration of robots within assembly plants introduces variability in energy consumption. Factors such as specific robot tasks, variations in product mix or production volumes, and the condition of robot components can all influence energy usage. In order to effectively address these uncertainties, it is essential to formulate appropriate constraints as chance constraints. By incorporating chance constraints, the model can consider the probabilistic nature of energy usage and ensure (open full item for complete abstract)

Bachelor of Arts, Wittenberg University, 2023, Computer Science

This paper aims to present a study on developing a program that assists nonprofit organizations in determining the ideal location for building their facilities based on community needs, thus maximizing their potential for success. The study highlights the importance of location in the success of nonprofit organizations, and the challenges they face in identifying suitable areas for their operations. The paper reviews existing literature on nonprofit organizations, location analysis, and data analysis techniques, and proposes a methodology for developing the program. The methodology involves data collection and analysis, and machine learning algorithms to predict community needs. The program provides a user-friendly interface for nonprofit organizations to access and analyze the data and offers recommendations for suitable locations based on their criteria. The study concludes that the proposed program can be a valuable tool for nonprofit organizations to make informed decisions about their location and maximize their potential for success in serving their communities.

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

This dissertation addresses energy-efficient operations for a fleet of diverse electrified vehicles at two system levels, the single-vehicle powertrain system, and the multi-vehicle transportation system, contributing to both with optimal control- and heuristic-based integrative approaches. At the single vehicle powertrain level, an electrified powertrain exhibits a continuum of complexities: mechanical, thermal, and electrical systems with nonlinear, switched, multi-timescale dynamics; algebraic and combinatorial path constraints relating a mix of integer- and real-valued variables. For optimal energy management of such powertrains, “PS3” is proposed, which is a three-step numerical optimization algorithm based on pseudo-spectral collocation theory. Its feasibility, convergence, and optimality properties are presented. Simulation experiments using PS3 on increasingly complex problems are benchmarked with Dynamic Programming (DP). As problem size increases, PS3's computation time does not scale up exponentially like that of DP. Thereafter, PS3 is applied to a comprehensive 13-state 4-control energy management problem. It saves up to 6% energy demand, 2% fuel consumption, and 18% NOx emissions compared to coarsely-modeled DP baseline. For generalizability, parallel and series electrified powertrain architectures running various urban delivery truck drive cycles are considered with multi-objective cost functions, Pareto-optimal study, energy flow analyses, and warm versus cold aftertreatment-start transients. At the multi-vehicle fleet level, energy-efficient vehicle routing approaches lack in integrating optimal powertrain energy management solutions. Extending single vehicle PS3 algorithm for a multi-vehicle fleet of plug-in hybrid (PHEV), battery electric (BEV), and conventional engine (ICEV) vehicles, an integrative optimization framework to solve green vehicle routing with pickups and deliveries (PDP) is proposed. It minimizes the fleet energy consumption a (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2022, Industrial and Systems Engineering

Concerns over global warming and a push for energy independence has caused an enormous investment in renewable energy resources. Transition to a power-system with significant amount of variable renewable energy generation requires adjustments to operation of power-system and use of new technologies to maintain its reliability. While the cost of battery energy storage has been declining significantly in recent years it is only economical for certain applications. Other form of energy storage is essential in transitioning to a power-system with 100% renewable energy resources. This dissertation investigates three issues arising in power-system and electricity market in transitioning to a power-system with high penetration of variable energy generation. The first topic studies the short-run impact of natural-gas prices on the reliability of power-system as it is transitioning away from coal-fired generation. Fuel prices can impact short-run power system operations, by changing the merit order between different generating units, which can affect system reliability. We developed a three-step process to examine these types of impacts. In the second topic we examine the benefit of conducting interim recommitment between day-ahead unit commit- ment and real-time dispatch, in a power-system with high penetration of wind. The last chapter examines the potential for using the flexibility of an aggregation of tank electric water heaters as a source of virtual energy storage. Specifically, we examined the operational performance of and operating profit that is earned by a fleet of water heaters that provide energy shifting and frequency regulation. We contrast this performance and profit to that of a lithium-ion battery.

Doctor of Philosophy, The Ohio State University, 2023, Chemical Engineering

The growing ecological footprint of human activities and large-scale industrialization have brought Earth and its natural eco-systems to a precarious state. Progress towards a Sustainable Circular Economy (SCE) is crucial to mitigate exploitation of natural resources, curb climate change and reduce accumulation of man-made materials in the environment as pollution. An optimal ‘roadmap' to facilitate the transition to a SCE, and to limit global temperature rise in the next 30 years to 2oC needs to be found. This dissertation focuses on developing mathematical frameworks and optimization tool-kits to holistically design current and future value-chain networks of products and services for SCE. These frameworks are demonstrated for case-studies pertaining to the transition of plastic packaging networks towards SCE. Utilizing process systems engineering and data analytics along with life cycle assessment, optimal value-chain pathways are found considering the environmental, economic, and social aspects of potential alternatives. A multi-objective superstructure optimization framework is developed to quantify the trade-offs between these SCE objectives in the form of Pareto-fronts. Applied to the grocery bags' life-cycle, this framework is able to quantify the paper-plastic dilemma; explore trade-offs between climate-change and recycling; and find emission hot-spots in current value-chains. Further, a novel multi-scale framework is developed to evaluate ‘green' chemical reaction-separation networks based on their interactions with the life-cycle and economy scales, thereby providing a tool to design systemic transformations of the chemical industry towards SCE. These frameworks are combined within a rigorous screening and ranking methodology to guide emerging technologies, climate actions using multi-objective metrics, and discover novel synergies between technology and policy-action. Finally, a stochastic portfolio optimization and planning framework is developed to gene (open full item for complete abstract)

Master of Science, The Ohio State University, 2022, Materials Science and Engineering

Recent increases in computational power have led to growing enthusiasm about the volume of data that can be collected and analyzed for many applications. However, the amount of data some physical/virtual systems generate is so great that an increased reliance on mathematical, statistical, and algorithmic based approaches to analyze and make decisions from the data is required. Application of these computational tools can lead to sharper decision making and vast amounts of knowledge discovered. The abstraction of the scientific decision-making process has led many researchers to consider observing systems with more tunable experimental parameters. This makes traditional experimentation, which is based on human researchers conducting the experiment and using their intuition to drive the next set of experiments, intractable for these applications. Autonomous experimentation (AE) systems, which are also a byproduct of the computational explosion, are able to address this issue and have found use across the fields of biology, chemistry, and materials science. AE systems are typically capable of conducting certain types of experiments with lower and more reliable turnaround times as opposed to their human counterparts. The automated execution of experiments naturally leads one to think about how those experiments can be parallelized and otherwise completed faster due to the lack of human presence in the experimentation environment. Therefore, AE systems are considered when designing many high-throughput experimentation (HTE) efforts. This thesis presents an overview of the current state-of-the-art for AE systems in Chapter 1, a framework developed to increase the independence of AE systems from human assistance in Chapter 2, and a machine-learning (ML) data processing pipeline that automates the image post-processing phase of the analysis of backscattered-electron scanning electron microscope images in Chapter 3.

Doctor of Philosophy, The Ohio State University, 2022, Business Administration

This dissertation addresses the relative importance of price and non-price criteria in sourcing decisions from three distinct perspectives. Each essay is motivated by the same problem: that organizations tend to unintentionally overweight cost minimization objectives in their sourcing decisions. In the first of three essays, I show that excessively price-based decision-making is a widespread problem in sourcing. To do this, I combined two sources of data on contract awards by the US federal government. I applied coarsened exact matching to identify cases where contracts were awarded using different criteria in similar situations. I then used logistic regression to show that when non-price criteria are weighted more heavily, the same contractor is more likely to receive awards for similar work in the future. This relationship is absent when there is a requirement for the decision-maker to provide written justification for the use of the more price-based approach, allowing me to infer a solution to the problem identified. In the second essay, I investigate whether the procurement profession's identity influences the relative importance of price in supplier selection decisions. I first conducted a series of semi-structured interviews with current practitioners, eliciting their comments on: their level of identification with the procurement profession; procurement's group image; others' perceptions of procurement's group image; and, procurement's status within their organization. Drawing from the observed variation in responses, I designed and conducted a scenario-based experiment. I find that strong identification with the procurement profession can contribute to more price-based sourcing decisions. In the third essay, I expand my focus from procurement professionals to a broader set of professions that commonly contribute to sourcing decisions: supply management, engineering, and marketing. Seeking to understand how these different perspectives influence (open full item for complete abstract)

Doctor of Education (Ed.D.), Bowling Green State University, 2022, Leadership Studies

Researchers have identified an academic insufficiency in investigating leadership during in-extremis situations both by emphasis and through difficulty in researching real-time events. These situations can and do commonly occur in settings involving the military and domestic safety forces such as police, fire, and emergency medical teams (EMS). This research has defined in-extremis circumstances as when the participants, whether civilians caught up in the circumstances, first responders to emergency incidents, or military personnel involved in combat situations are vulnerable to incurring significant injuries up to and including death. In plainer words, when people's lives are on the line and the decisions and actions performed during the event could greatly impact the outcome. This research utilized a mixed-methods design gathering online quantitative data from 401 fire officers (grouped into Exemplars and General Fire Officers) and qualitative data from a Delphi panel of Exemplars only. A purpose of this mixed-methods study was to investigate how career fire officers who were identified by their fire departments as exemplars in field command reported they make critical decisions during in-extremis moments and to explore whether there are commonalities in their leadership approaches. This was attained through a Delphi panel composed of 14 Exemplar fire officers. Three rounds of semi-structured interviews were conducted that attempted to reach consensus among the Delphi panel members. In addition, the Rational-Experiential Inventory-40 (REI-40) was offered online to 17 career fire department officers to help evaluate their tendencies towards rational/analytical and experiential/intuitive thinking. Results from the online REI-40 survey and findings from the Delphi interviews revealed that the Exemplars rely upon their experience and intuition to a greater extent and rely less upon written procedures than did their General Fire Officer counterparts. The Delphi p (open full item for complete abstract)