Frequency Domain Experiments (FDEs) were first used in discrete-event simulation to perform system parameter sensitivity analysis for factor screening in stochastic system simulations. FDEs are based on the intuitive assertion that if one or more system parameters are oscillated at fixed frequencies throughout a simulation run, then oscillations at the same frequencies will be induced in the system's response. Spectral (Fourier) analysis of these induced oscillations is then used to characterize and analyze the system. Since their introduction 12 years ago, significant work has been done to extend the applicability of FDEs to regression analysis, simulation optimization and gradient estimation. Two fundamental theoretical and data analysis FDE problems remain, however. Both problems are addressed in this dissertation.;To perform a FDE Fourier analysis, a sampled data sequence of response observations is used; i.e., the selected system response is sampled using a suitable oscillation (sampling) index. The choice of an appropriate oscillation index is an open problem in the literature known as the FDE indexing problem. This dissertation presents a solution to the FDE indexing problem. Specifically, a FDE Fourier data analysis algorithm is developed which uses the simulation clock as the oscillation index. This algorithm is based on the well-established theory of counting (Poisson) processes. The algorithm is implemented and tested on a variety of systems including several networks of nonstationary M/G/1 queues.;To justify the use of Fourier methods, a basic FDE model assumption is that if a particular system response statistic is sensitive to a system parameter, then sinusoidal variation of that system parameter at a fixed frequency will induce similar sinusoidal variations in the response statistic, at the same frequency. There is, however, a lack of theoretical support for this model assumption. This dissertation provides some of that theoretical support; i.e., the FDE Fourier data analysis algorithm developed in this dissertation is used to analyze the frequency response of a M/M/1 queuing system. An equation is derived which accurately characterizes the extent to which the departure process from a M/M/1 queuing system can be modeled as an amplitude-modulated, phase-shifted version of the oscillated arrival process.

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There is a dearth of research on the application of hard Operational Research (OR) techniques (simulation, linear programming, goal programming, etc.) in determining optimal ordering, inventory and allocation policies for goods within distribution systems in developing countries. This study aims to assist decision making at a not-for-profit organization (NPO), Foodbank South Africa (FBSA), within its allocation system through a combined ‘soft-hard’ OR approach. Two problem-structuring tools (soft OR), Causal Mapping (CM) and Soft System Methodology’s Root Definitions (RDs), are used to structure the organization's goals (in order to gain a comprehensive understanding of the decision-context) and gain a better understanding of the ‘decision-issues’ in the allocation system at its Cape Town warehouse.

The exposure of companies to turbulence, uncertainty, and vulnerability in their supply chain results in supply chain disruption with an estimate cost of $10 million for each supply chain disruption. The purpose of this case study was to explore the strategies supply chain managers use to mitigate supply chain disruption on business performance in a pharmaceutical company in Maryland. Contingency theory of fit formed the conceptual framework for this study. Participant perceptions were elicited in interviews with 11 supply chain managers regarding strategies to mitigate risks associated with supply chain disruptions. Data from interviews and supporting documents were processed and analyzed using data source triangulation to discern emergent themes. Three main themes emerged: (a) supply chain design, planning, and forecasting; (b) flexible and multiple supplier base; and (c) resource allocation and demand management. The implications for positive social change include the potential of reducing supply chain risk, which could lead to lower prices of products for consumers, increased stakeholder satisfaction, and a higher standard of living.

Agent-based modeling (ABM) is a relatively new tool for use in electric power market research. At heart are software agents representing real-world stakeholders in the industry: utilities, power producers, system operators, and regulators. Agents interact in an environment modeled after the real-world market and underlying physical infrastructure of modern power systems. Robust simulation laboratories will allow interested parties to stress test regulatory changes with agents motivated and able to exploit any weaknesses, before making these changes in the real world. Eventually ABM may help develop better understandings of electric market economic dynamics, clarifying both delineations and practical implications of market power. The research presented here builds upon work done in collateral fields of machine learning and computational economics, as well as academic and industry literature on electric power systems. We build a simplified transmission model with agents having learning capabilities, in order to explore agent performance under several plausible scenarios. The model omits significant features of modern electric power markets, but is able to demonstrate successful convergence to stable profit-maximizing equilibria of adaptive agents competing in a quantity-based, available capacity model.

Psychological flow is a positive mental state where one is so fully concentrated in a challenging task that self-consciousness falls away, time seems to stand still, and the reward is the experience of meeting the challenge. Previous research on flow in the workplace has been performed on how to create conditions to promote its occurrence in workers, to describe its attendant individual and organizational benefits, and to measure it through self-reported means and physiologically. Such research has been focused on creative endeavors (such as the arts, sports, medicine, teaching), where individuals have high agency over the execution of activities needed to successfully complete the work. This research focuses on flow in back-office transactional work, which has been little studied to date. Transactional work are those tasks that are largely rote, repetitive, and prescribed by standardized procedures, leaving little room for agentic options. Examples of such work include data entry and bookkeeping A theory is next discussed that offers the notion of a holistic system of non-task variables working together with job tasks to create conditions conducive to increasing the likelihood of transactional workers experiencing flow. Flow will next be compared to similar constructs and their relatedness to flow will be discussed. Various flow measurement methods will be presented, along with their advantages and disadvantages. These discussions set the stage for the present set of qualitative and quantitative research efforts, whose objective is to offer support for the holistic work system approach to creating flow. First, a phenomenological study of flow in transactional workers is presented, where their lived experiences of flow are documented and the extent to which certain non-task work system variables support the occurrence of flow. Next, a proof-of-concept laboratory experiment is reviewed, where seat comfort (a non-task work system factor) is shown to be a first-order influencer of flow in the study's participants. Finally, the results of a designed experiment incorporating multiple non-task work system factors are presented and the interaction of high seat comfort and low computer screen contrast are shown to directly impact the occurrence of flow in that study's participants. Flow is also shown to predict productivity improvements in participants when combined with high seat comfort and low computer screen contrast. Additionally, certain physiological functions thought to correlate to flow are selected and measured in the participants. Lower heart rate variation partially correlates to flow. The results are applicable to the design of holistic work systems in organizations employing back-office transactional workers. Recommendations for future research are presented that will strengthen and build on the current results.

Our research considers job scheduling, a special type of resource assignment problem. For example, at a cross-docking facility trucks must be assigned to doors where they will be unloaded. The cargo on each truck has various destinations within the facility, and the unloading time for a truck is dependent on the distance from the assigned door to these destinations. The goal is to assign the trucks to doors while minimizing the amount of time to unload all trucks.;We study scheduling algorithms for problems like the cross-docking example that are different from traditional algorithms in two ways. First, we utilize real-time, where the algorithm executes at the same time as when the jobs are handled. Because the time used by the algorithm to make decisions cannot be used to complete a job, these decisions must be made quickly Second, our algorithms utilize lookahead, or partial knowledge of jobs that will arrive in the future.;The three goals of this research were to demonstrate that lookahead algorithms can be implemented effectively in a real-time context, to measure the amount of improvement gained by utilizing lookahead, and to explore the conditions in which lookahead is beneficial.;We present a model suitable for representing problems that include lookahead in a real-time context. Using this model, we develop lookahead algorithms for two important job scheduling systems and argue that these algorithms make decisions efficiently. We then study the performance of lookahead algorithms using mathematical analysis and simulation.;Our results provide a detailed picture of the behavior of lookahead algorithms in a real-time context. Our analytical study shows that lookahead algorithms produce schedules that are significantly better than those without lookahead. We also found that utilizing Lookahead-1, or knowledge of the next arriving job, produces substantial improvement while requiring the least effort to design. When more lookahead information is used, the solutions are better, but the amount of improvement is not significantly larger than a Lookahead-1 algorithm. Further, algorithms utilizing more lookahead are more complex to design, implement, and analyze. We conclude that Lookahead-1 algorithms are the best balance between improvement and design effort.

Modern Organizations must be high performing to sustain and grow their operations. This is particularly true for healthcare organizations as they have complex and sensitive operations. One way to improve organizational performance is to adopt Quality Management Systems (QMSs). QMSs organize and improve the effectiveness of all processes to meet stakeholder requirements and achieve organizational performance improvements in alignment with strategic goals. However, the value of QMSs is dependent on successful implementation, which is reportedly quite challenging. This doctoral research examines the factors that affect the successful implementation of QMSs in healthcare. First, a systematic analysis of the published literature revealed that this area is at a relatively early to moderate stage of maturity with many significant opportunities to advance the research area. After that Thematic Analysis of the factors studied in the literature identified ten success factors and one implementation outcome. The ten emergent factors interrelationships and effect on the outcomes was investigated using multiple linear regression and correlation analysis. The result revealed three Critical Success Factors (CSFs), Implementation Culture, Structure, and Management Training. The research found that the Implementation culture requires understanding the working environment with all stakeholders to recognize quality as a routine rooted in all the processes. A systematic embedding of quality within the structure of the organization includes reviewing processes and measure their quality. Management training entitles that change toward QMS requires proper knowledge and expertise; otherwise, it will lead to failure. Hence, decision-makers' ability to understanding the QMS is crucial for success. The study also revealed that a full understanding of the interaction among emergent factors is essential to fully improve the chances of QMSs' implementation success in healthcare, making the potential benefits of these systems more broadly accessible to support this critical industry.

Project manufacturing is a production manufacturing mode that develops and produces large and complex systems that is supplemented by project management techniques for planning and execution. Project manufacturing is better understood by examining it along with three other key areas: manufacturing, project management, and team management. Critical success factors for each of these areas have been examined in the literature but have not been examined in a real world setting in the aerospace and defense industry. This research examines the association between perspectives of project teams and senior management on critical success factors along the four identified dimensions of success in the literature: efficiency, impact on consumer, impact on team, and preparation for the future. These associations are evaluated using Spearman rank correlation method that utilizes survey data obtained from ten project manufacturing teams at an aerospace and defense company along the identified four dimensions of success stated. This research also utilizes regression method to identify significant predictors of overall project success in the aerospace and defense industry for project manufacturing teams. The main focus of this research was achieved by surveying project team members to indicate their level of agreement with the identification of critical success factors. Performance scores for each dimension of success were also gathered from senior management in order to conduct the appropriate statistical analysis of the two sets of paired scores. This research demonstrates that there are both considerable agreements and disagreements between project team members and senior management on project success on different factors. This research also provides some recommendations to bridge the gap between these two groups.

The Aerospace and Defense industry has shifted into a global competitive market that is prioritizing innovative advancements in technological capabilities. Corporations are now having to further develop customer focused strategies based in adding value while reducing costs. Large manufacturing corporations often embrace continuous improvement methodologies, such as Lean Six Sigma, for process improvement. Many organizations have received minimal benefit from the methodology which may link back to leadership and culture. This research examined which styles of leadership are most effective when trying to gain the most value from Lean Six Sigma within manufacturing. The research study surveyed 112 black belt practitioners from one large Aerospace and Defense organization with multiple manufacturing locations in the United States. The study analyzed the relationship between laissez-faire, transactional, and transformation leadership styles and the Lean Six Sigma critical success factors of top management, project selection, and training. The results found that both transactional and transformational leadership styles had a positive correlation while the laissez-faire leadership style had a negative correlation. The results also found that laissez-faire, transactional, and transformational leadership did not predict the success of LSS implementation These findings demonstrate black belt practitioners with transactional and transformational leadership styles positively influence the benefits derived from Lean Six Sigma implementation.