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This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model's robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to public safety. The second chapter proposes an innovative mathematical model which captures several essential features (e.g., the capacity of the facility and individual choices, heterogeneity of individual behavioral and choice sets, restriction on choice sets depending on the location of the shooter and facility orientation, and many others) which are essential for appropriately characterizing and analyzing the response strategy for civilians under an AS exposed environment. We demonstrate the applicability of the proposed model by implementing the effectiveness of the RUN.HIDE.FIGHT.® (RHF) program in an academic environment. Given most of the past incidents took place in built environments (e.g., educational and commercial buildings), there is an urgent need to methodologically assess the safety of the buildings under an active shooter situation. Finally, the third chapter aims to bridge this knowledge gap by developing a learning technique that can be used to model the behavior of the shooter and the trapped civilians in an active shooter incident. Understanding how the civilians responded to different simulated environments, a number of actions could have been undertaken to bolster the safety measures of a given facility. Finally, this study provides a customized decision-making tool that adopts a tailored maximum entropy inverse reinforcement learning algorithm and utilizes safety measurement metrics, such as the percentage of civilians who can hide/exit in/from the system, to assess a workplace's safety under an active shooter incident.

Lattices are an easy and clean class of periodic arrangements that are not only discrete but associated with algebraic structures. We will specifically discuss applying lattices theory to computing the area of polygons in the plane and some optimization problems. This thesis will details information about Pick's Theorem and the higher-dimensional cases of Ehrhart Theory. Closely related to Pick's Theorem and Ehrhart Theory is the Frobenius Problem and Integer Knapsack Problem. Both of these problems have higher-dimension applications, where the difficulties are similar to those of Pick's Theorem and Ehrhart Theory. We will directly relate these problems to optimization problems and operations research.

Nuclear energy is a source of carbon free power. With many countries striving to make deep carbon cuts in their energy sectors, nuclear energy could be a large part of the solution. One of the main obstacles standing in the way of the use of nuclear energy is the issue of used nuclear fuel disposal. According to the NEI, the U.S. creates about 2000 metric tons of used nuclear fuel per year and has generated around 76,000 metric tons of used nuclear fuel over the last 4 decades. While there are technical problems that need to be solved, it is primarily the public and political opposition to the disposal of used nuclear fuel that stands in the way of progress in this area. This work addresses this issue through Multi-Criteria Decision Analysis (MCDA). To make a decision among ten different fuel cycles, we have brought together five stakeholders: Nuclear Scientists and Engineers, Environmental Scientists, Economists, Political Scientists, and The General Public. Using Multi-Attribute Value Theory (MAVT), we have been able to develop decision models for each stakeholder as well as a model that combines them all and came to the conclusion that of the ten fuel cycles considered, the best decision is to continue to use On Site Dry Cask Storage. This decision is made with small sample sizes but the methodology could be applied at much larger scales and can potentially be used to choose a fuel cycle that encounters much less political and social opposition to its implementation.

Nous détaillons dans cette thèse des algorithmes modérément exponentiels pour l'approximation du problème MAX SAT. Nous discutons d'une méthode générique pour la conception d'algorithmes exponentiels réalisant des schémas d'approximation dans un cadre plus général. Enfin, nous présentons des résultats paramétrés pour des problèmes de coupe à cardinalité contrainte. / We give in this thesis some moderately exponential algorithms for the MAX SAT problem. We discuss a very general method to conceive efficient exponential algorithms that give approximation scheme. In the end, we present some parameterized results for CUT problem with constrained cardinality.

Les travaux de recherche menés dans cette thèse s’inscrivent dans deux champs disciplinaires que sont l’évaluation du confort et l’aide multicritère à la décision.L’objectif de la thèse est de construire un modèle pour évaluer des trains sur le point de vue du confort tel qu’il est perçu par les voyageurs. L’approche utilisée pour cela repose sur trois étapes principales de construction d’un modèle d’aide multicritère à la décision. La première consiste à définir et à formaliser les critères de confort du problème. Dans la deuxième étape, il s’agit de construire les échelles afin de pouvoir évaluer les trains sur chaque critère de confort considéré.La troisième étape consiste à agréger les critères de confort en utilisant des méthodes d’agrégation multicritère. Cette étape nécessite l’élicitation des préférences des décideurs afin de mettre en oeuvre les méthodes d’agrégation.Notre contribution est de formaliser une approche pour la construction d’un modèle d’évaluation du confort dans les trains. Cette approche peut être appliquée à d’autres problématiques que l’évaluation du confort. Elle présente deux particularités principales. La première est d’intégrer dans la construction du modèle des facteurs importants traduisant la perception du confort. Nous avons choisi pour cela un modèle hiérarchique comportant plusieurs niveaux. La deuxième particularité de l’approche est d’utiliser des méthodes d’agrégation pouvant être différentes d’un noeud à un autre du modèle. Elle présente également d’autres aspects plus spécifiques, notamment lors de l’élicitation des préférences où nous construisons des exemples d’apprentissage informatifs pour accélérer le processus d’élicitation / This PhD thesis falls within two scientific areas, which are comfort evaluation and multiple criteria decision aiding. The main purpose is to develop a model in order to evaluate trains on the comfort point of view, as percieved by passengers. The developed approach is based on three main steps of developing a multiple criteria decision aiding model. The first one consists on defining and formalizing the criteria of comfort. In the second step, the scales of each considered criterion must be built in order to evaluate the trains on these last. The third step aims at aggregating the criteria, using multiple criteria aggregation methods, in order to obtain an overall comfort evaluation of trains. For this purpose, the decision maker’s preferences must be elicited

Private sector U.S. defense contractors failing to meet contract objectives experience lower profitability, pay costly penalties, and risk survivability. Using the theory of contracts, the purpose of this multiple case study was to the explore strategies that some leaders of U.S. defense contracting businesses use to meet all the contract terms with the U.S. Department of Defense. Data were collected from 5 leaders of private sector defense contracting companies in northwest Florida through face-to-face, semistructured interviews and through a review of operations manuals, quality assurance policy manuals, and archived U.S. Department of Defense contracts. During data analysis using Yin's 5-step process of compiling, disassembling, reassembling, interpreting, and concluding the data, 4 themes on strategies emerged: (a) communication strategy for successful completion of contracts, (b) technology strategy to monitor contract compliance, (c) training strategy for program managers, and (d) subcontractor selection strategy. The findings indicated that communication among all contract parties was an essential component of each of the 4 themes. Computerizing the contracting workflow to monitor compliance efforts, training program managers for effective oversight of contract compliance, and selecting subcontractors were vital elements of the strategies private sector defense contractors used to meet all the terms and conditions of U.S. Department of Defense contracts. The implications for positive social change include the potential for private sector defense contractors to improve the strength of the defense of the northwest Florida community, lower unemployment, and provide a safer environment for humanity.

3DP (three-dimensional printing) technologies have become more than just a tool to help companies with prototyping and designing in the pre-production stage. Some firms have already implemented 3DP technology to produce parts and end-use products. However, there are several challenges and barriers that this technology must overcome to replace traditional manufacturing methods. One of the most significant obstacles associated with 3D printing is its low level of accuracy in variable repeatability when it comes to making separate batches of the same product. There are several arguable reasons behind this variation. Some of the factors that can influence repeatability are the type of material, the design, the type of product produced, and the orientation, or the location of the build inside the building envelope. The goal of this study was to determine whether the location of the build inside the surface area of the working envelope can affect the properties (height, width, depth, and weight) of the product. Western Kentucky University (WKU) provides students with a few 3D printers on campus. One of those printers, a Stratasys (model: BST 768/SST 768), is in the Senator Mitch McConnell Advanced Manufacturing and Robotics Laboratory. The researcher used this printer for the study to determine if the location of the printer influenced the final product. The conclusion of the research did reveal that the printing location does affect the quality of the final product.

Balancing trade-offs between production cost and holding cost is critical for production and operations management. Utilization of an operating room affects production cost, which relates to makespan, and patient flowtime affects holding cost. There are trade-offs between two objectives, to minimize makespan and to minimize flowtime. However, most existing constructive heuristics focus only on single-objective optimization. In the current literature, NEH is the best constructive heuristic to minimize makespan, and LR heuristic is the best to minimize flowtime. In this thesis, we propose a current and future deviation (CFD) heuristic to balance trade-offs between makespan and flowtime minimizations. Based on 5400 randomly generated instances and 120 instances in Taillard’s benchmarks, our CFD heuristic outperforms NEH and LR heuristics on trade-off balancing, and achieves the most stable performances from the perspective of statistical process control.

Considering the significant investment of higher education made by students and their families, graduating in a timely manner is of the utmost importance. Delay attributed to drop out or the retaking of a course adds cost and negatively affects a student’s academic progression. Considering this, it becomes paramount for institutions to focus on student success in relation to term scheduling. Often overlooked, complexity of a course schedule may be one of the most important factors in whether or not a student successfully completes his or her degree. More often than not students entering an institution as a first time full time (FSFT) freshman follow the advised and published schedule given by administrators. Providing the optimal schedule that gives the student the highest probability of success is critical. In efforts to create this optimal schedule, this thesis introduces a novel optimization algorithm with the objective to separate courses which when taken together hurt students’ pass rates. Inversely, we combine synergistic relationships that improve a students probability for success when the courses are taken in the same semester. Using actual student data at the University of Kentucky, we categorically find these positive and negative combinations by analyzing recorded pass rates. Using Julia language on top of the Gurobi solver, we solve for the optimal degree plan of a student in the electrical engineering program using a linear and non-linear multi-objective optimization. A user interface is created for administrators to optimize their curricula at main.optimizeplans.com.