Blockchain technology is a new digital technology that has been disrupting the way businesses are performing. It is a decentralized and distributed ledger that enables transactions of any form of value. As blockchain technology provides visibility, transparency, and security through the multi-agent system, the supply chain sector is one of its critical and promising applications. In a highly dynamic environment, the supply chain's efficiency needs to be measured from a blockchain perspective. As the main benefit of blockchain technology is the visibility and real-time access to data, blockchain technology's preeminent affected areas within the supply chain are the responsiveness to the customer and the inventory efficiency among the supply chain partners. This research developed a dynamic model to measure supply chain efficiency from the blockchain perspective. The developed model is based on system dynamics methodology to model a typical three-tier supply chain; manufacturer, distributor, and retailer. It consists of three components; chain system, backlog system, and supply chain efficiency evaluation system. First, an introduction to the supply chain and blockchain technology is provided. Second, a literature review of supply chain and blockchain technology is presented. From the literature analysis, a research gap is identified with the research questions and objectives. Third, the methodology proposed in this research to answer the research problems is discussed with a literature review about applying system dynamics methodology within the supply chain, and technology adoption is provided. Forth, an illustration of the proposed model is given. Fifth, the dynamic analysis of the supply chain's performance according to various scenarios is evaluated. Finally, a conclusion with the future work is provided.

This dissertation seeks to design an optimization algorithm, based on naturalistic flight data, with emphasis on safety to perform a benefits' analysis when sequencing and scheduling aircraft at the runway. The viability of creating a decision-support tool to aid air traffic controllers in sequencing and optimizing airport operations is evaluated through the benefits' analysis. Air traffic control is a complex and critical system that ensures the safe and efficient movement of aircraft within the airspace. This is particularly true in the immediate vicinity of an airport. Unlike in en-route or terminal area airspace where aircraft usually traverse well established routes and procedures, near the airport after completing a standard arrival procedure, the routes to the final approach are only partially defined. With safety being the foremost priority, the local tower controllers monitor and maintain separation between aircraft to prevent collisions and ensure the overall safety of the airspace. This involves constant surveillance, coordination, and decision-making to manage the dynamic movement of aircraft, changing weather conditions, and potential hazards. All the while, the controllers make decisions regarding tromboning or vectoring based on various factors, including traffic volume, airspace restrictions, weather conditions, operational efficiency, and safety considerations to ensure a safe traffic sequencing of aircraft at the runway. A novel framework is presented for modeling, characterizing, and clustering aircraft trajectories by extracting traffic control decisions of air traffic controllers. A hidden Markov model was developed and applied to transform trajectories from a sequence of temporal spatial position reports to a series of control actions. The edit distance is utilized for quantifying the dissimilarity of two variable-length trajectory strings, followed by the application of k-medoids algorithm to cluster the arrival flows. Next, a repeatable process for detecting and labeling outlier trajectories within a cluster is introduced. Through application on a set of historical trajectories at Ronald Reagan Washington National Airport (DCA), it is demonstrated that the proposed clustering framework overcomes the deficiency of the classical approach and successfully captures the arrival flows of trajectories, that undergo similar control actions. Leveraging on the set of arrival flows, statistical and machine learning models of air traffic controllers are created and evaluated when ordering aircraft to land at the runway. The potential inefficiencies are identified at DCA when sequencing aircraft. As such, there is a potential performance gap, and it appears that there is room for additional sequence optimization. With the goal of overcoming the potential inefficiencies at DCA, a mixed-integer zero-one formulation is designed for a single runway that takes into consideration safety constraints by means of separation constraints between aircraft imposed at each metering point from the entry to the airspace until landing. With the objective of maximizing runway throughput and minimizing the traversed distance, the model sequences and schedules arrivals and departures and generates safe and conflict-free arrival trajectories to actualize that scheduling. The output of the optimization shows that the model successfully recovers approximately 52% of the performance gap between the actual distance traversed and idealized (cluster centroids) distance traversed by all arrival aircraft. Moreover, each arrival aircraft, on average, traverses 2.12 nautical miles shorter than its historical trajectory and thus saving approximately 10 US gallons of jet fuel. By showcasing the potential benefits of the optimization, this dissertation takes a step towards achieving the long-term vision of developing a decision-support tool to assist air traffic controllers in optimally sequencing and scheduling aircraft. To fully leverage the potential benefits of optimization, further development and refinement of the algorithm are necessary to align it with real-world operational demands. As future work, the research would be expanded to integrate uncertainties like weather conditions, wind directions, etc. into the optimization.

Business incubators and entrepreneurship centers are essential tool of economic and social development. This mix method research examined and described the effectiveness of business incubators and entrepreneurship centers as supporting mechanism for economic and social development. The research investigated the impact of business incubators and entrepreneurship centers on mainly combined revenue, regional per capita GDP, and regional unemployment rate in 40 states. The quantitative data was collected by InBIA and UCF in a period of 42 months. 206 of business incubators and entrepreneurship centers responded to a lengthy survey that contains 123 questions. Moreover, 20 semi-structured interviews were conducted with business incubator managers to collect the qualitative data that compliment the quantitative data. The research was design into three main phases: conceptual phase, operational phase, and conclusion. Eisenhart (1989) building theories from case study research approach was applied to this research. The quantitative data was analyzed by using SmartPLS4 which adopted multiple liner regression. QDA Miner Lite software was used to analyze the qualitative data. The result showed that there are significant impact of business incubators and entrepreneurship centers on incubated companies combined revenue and regional per capita GDP. Whereas the effect of business incubators and entrepreneurship centers was not proofed.

For the mental and physical wellbeing of nuclear power plant (NPP) reactor operators (ROs) it is pertinent these work environments take advantage of automation, to an appropriate extent, to reduce workload and increase performance. With automation, RO resources can be better distributed to make sure NPP operations are running smoothly and efficiently. However, inappropriate automation may put ROs at risk of becoming complacent and slow to react, thus unable to perform their job in emergency situations. In this study students acted as NPP ROs and interacted with different tasks and levels of automation. Since NPPs are becoming more digitalized it is important to understand how these changes are going to affect operators' performance and perceived mental workload (MW). Individual differences are also considered, as not everyone is going to have the same reaction to these changes. Results of this study indicate that an increase in automation decreases time to react to the automation requesting input. However, there were significant differences between perceived MW such that higher MW was reported in the higher level of automation for checking and responding tasks. Personality traits can play a large role in how ROs respond to and work with automation. In this study, personality (i.e., Big 5) was not correlated to any MW measures but was positively correlated with perception of automation competence and usefulness in the lower automation condition. When compared with previous iterations of this study that had no automation, both low and high LOA significantly reduced perceived workload. This study's findings enhance awareness of individual differences and their implications on ROs' perceived MW and automation adoption and the importance of upcoming changes in NPPs to ensure optimized RO vigilance and performance.

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Procuring combat systems in the Department of Defense is a balancing act where many variables, only some under control of the department, shift simultaneously. Technology changes non-linearly, providing new opportunities and new challenges to the existing and potential force. Money available changes year over year to fit into the overall US Government budget. Numbers of employees change through political demands rather than by cost-effectiveness considerations. The intent is to provide the best mix of equipment to field the best force against an expected enemy while maintaining adequate capability against the unexpected. Confounding this desire is the inability of current simulations to dynamically model changing capabilities and the very large universe of potential combinations of equipment and tactics.;The problem can be characterized as a stochastic, mixed-integer, non-linear optimization problem. This dissertation proposes to combine an agent-based model developed to test solutions that constitute both equipment capabilities and tactics with a co-evolutionary genetic algorithm to search this hyper-dimensional solution space. In the process, the dissertation develops the theoretical underpinning for using agent-based simulations to model combat. It also provides the theoretical basis for improvement of search effectiveness by co-evolving multiple systems simultaneously, which increases exploitation of good schemata and widens exploration of new schemata. Further, it demonstrates the effectiveness of using agent-based models and co-evolution in this application confirming the theoretical results.;An open research issue is the value of increased information in a system. This dissertation uses the combination of an agent-based model with a co-evolutionary genetic algorithm to explore the value added by increasing information in a system. The result was an increased number of fit solutions, rather than an increase in the fitness of the best solutions. Formerly unfit solutions were improved by increasing the information available making them competitive with the most fit solutions whereas already fit solutions were not improved.

Optimization of electronic warfare (EW) receiver scan strategies is critical to improving the probability of surviving military missions in hostile environments. The problem is that the limited understanding of how dynamic variations in radar and EW receiver characteristics has influenced the response time to detect enemy threats. The dependent variable was the EW receiver response time and the 4 independent variables were EW receiver revisit interval, EW receiver dwell time, radar scan time, and radar illumination time. Previous researchers have not explained how dynamic variations of independent variables affected response time. The purpose of this experimental study was to develop a model to understand how dynamic variations of the independent variables influenced response time. Queuing theory provided the theoretical foundation for the study using Little's formula to determine the ideal EW receiver revisit interval as it states the mathematical relationship among the variables. Findings from a simulation that produced 17,000 data points indicated that Little's formula was valid for use in EW receivers. Findings also demonstrated that variation of the independent variables had a small but statistically significant effect on the average response time. The most significant finding was the sensitivity in the variance of response time given minor differences of the test conditions, which can lead to unexpectedly long response times. Military users and designers of EW systems benefit most from this study by optimizing system response time, thus improving survivability. Additionally, this research demonstrated a method that may improve EW product development times and reduce the cost to taxpayers through more efficient test and evaluation techniques.

In this dissertation, we elaborate on the inherent risks and uncertainties in power systems and associated industries, and develop practical solution methods to eliminate their adverse effects. our research agenda consists of practice-driven problems in different stages of power generation as follows. (1) Affordable fuel procurement through developing a comprehensive fuel supply chain design and operations planning system for electricity generation companies, (2) reliable electricity generation through incorporating dynamic asset rating concept in the unit commitment problem, and (3) efficient demand management through proposing a job scheduling model for effective local generation consumption. Since reliability cannot be compromised in energy sector, robust optimization has been adopted as a powerful method to model multiple sources of uncertainty, and to protect the performance of the systems against worst situations. Exact and heuristic methods are then developed and customized to solve these computationally challenging problems. In particular, inspired by the challenges in solving two-stage robust optimization problems, we developed a multi-scenario cutting plane generation algorithm, that considers all the realizations of the uncertainty set at once, and thus, alleviates the computational challenge.

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The job shop scheduling problem, in which we must determine the order or sequence for processing a set of jobs through several machines in an optimum manner, has received considerable attention. In this paper a number of the methods and techniques are reviewed and an attempt to categorize them according to their appropriateness for effective use in job shop scheduling has been made. Approaches are classified in two categories: a) analytical techniques and b) graphical methods. Also, it should be noticed that this report does not include all the attempts and trials, especially the heuristic approaches.