The main focus of this thesis is to test a model that compares the present grid and a Smart Grid system. The thesis discusses the major issues faced by our electricity infrastructure and the possible solutions offered by the Smart Grid. Present grid limitations based on operational, technological, planning, and policy issues are covered. The thesis initially focuses on the limitations of our present grid, and describes severe limitations of our current grid during blackouts. The thesis outlines possible solutions for these problems offered by the concept of the Smart Grid, whose technology and features are described in detail. The thesis details Smart Grid technologies for power generation and the latest electronic devices available to aid the current aging power grid. Further, this thesis offers an analysis that compares the ‘present grid’ to a particular ‘Smart Grid’ configuration consisting of a Combined-Heat & Power (CHP) plant, a Photovoltaic system, and a Demand Response with real-time pricing. The analysis reveals the economic and operational benefit of the Smart Grid system under consideration.

Timely access and patient-physician continuity are two key measures for a primary care practice. Timeliness refers to the ability to obtain a physician appointment as soon as possible. Patient-physician continuity is one of the hallmarks of primary care and refers to the ability to provide appointments with a patient's own physician as much as possible. In the last decade, a paradigm called "advanced access" has been adopted by many clinics, which encourages physicians "to do today's work today" rather than push appointments in the future. Advanced access necessitates the design effective capacity management policies. In this study we apply the ideas of process flexibility to capacity management in a primary care practice. This leads to a system where patients can be seen by their primary care provider or additional physicians, the latter incurring a cost due to reduction of efficiency. We model a practice with multiple physicians and their corresponding patient panels as a stochastic dynamic program. Patients call in throughout the day and a decision has to be made immediately whether to assign the patient to a specific physician or refuse her. The study consists of three parts: in the first, we investigate the general benefits and shortfalls of different implementations of physician flexibility; in the second, we develop heuristic scheduling policies that can be implemented in practice; the third part compares the benefits of the system currently used in practice with our proposed approach where physicians are chained and pairs of physician are familiar with a certain panel.

Wind is the second largest renewable energy source after solar. It is one of the fastest growing sources of electricity in the world and currently of wind energy is installed in the United States and an additional is under construction (Office of Energy and Environment Affairs, 2011). For the growth of wind electricity, one of the most prominent environmental concerns relates to the death of birds, bats and other avian species resulting from collision with turbine blades. This thesis develops a model that provides the optimal strategy of turning the turbines off in a wind farm for certain periods to mitigate bird mortalities. We first create a single turbine optimization model for each hour on each day of a single month. We maximize the expected revenue generation and limit the expected bird mortalities to a certain level to solve for the dates and times for which the turbine should be turned off. The optimization problem is found to be part of common class of problems called Knapsack problems and through experiments we conclude that a linear programming (LP) relaxation of the problem provides a near-optimal solution. We extend the single-turbine model to a multiple-turbine model applicable to a wind farm. In this case, we solve for the percentage of wind turbines that should be turned off to limit the expected bird mortalities to a certain level. Finally, we carry out an uncertainty analysis and estimate probability distributions over the outcome of optimal strategy of turning the turbine off. We consider the Cape Wind project as a case study and limit the analysis to only one species of endangered birds called the common loon. We find that in order to save an expected number of 10 such birds in the month of March; we need to turn the turbine off for a total of 23 hours spread over specific dates and times. The average cost per bird was found to be $171.

Emergency Departments (ED) in the US are in crisis. In 2004 between 40 and 50% of the US hospitals experienced crowding. Crowding negatively affects the quality and access of emergency care. Optimally managing the present resources in EDs and finding ways to reduce patient length of stay is critical to improving patient care in EDs. We use computer simulation modeling of patient flow in an ED (i.e. discrete event simulation) to study the impact of 1) resource management and profit optimization, 2) process changes, and 3) task prioritization on patient length of stay. Our model of patient flow, ED with multiple Acuities (EDWA), consists of a five-fold process of emergency care which represents five different types of patients. This model assigns each patient to an individual doctor and allows patient treatment process to proceed simultaneously or sequentially with other activities. The results show an increase in the need for resources with an increase in variability; the dependency of the optimal resource mix on the objective we want to optimize; and the relationship between quality of emergency care and profitability. Further, our findings imply that bedside registration (a commonly recommended process change in EDs) should only be implemented when emergency department beds are free.

A survey of project managers and requirements engineers was conducted to determine what skills, qualifications, and experiences were associated with project success. Survey results indicated that projects using Joint Application Development (JAD) sessions, use cases, and prototypes to engineer requirements were most successful. Other indicators of project success, according to participants, included an adequate allotment of time for requirements engineering—at least 15% of a project's time—and the use of project managers and requirements engineers with professional work experience. In particular, data indicated that Project Managers with at least five years of experience in Information Technology resulted in more successful projects.

Traffic and parking congestion are significant issues at many universities nationwide. The delays experienced result in wasted time, money, and fuel for students, faculty and staff, not to mention the negative contributions to the environment. This paper quantifies the amount of vehicle emissions generated during an average morning peak hour in the university environment. Using VISSIM and CMEM microsimulation packages, a model is created for California Polytechnic State University- San Luis Obispo to aggregate the collective transportation behaviors and practices of the campus and recognize the implications these behaviors pose on the transportation network as a whole. Reasonable estimates are generated for overall HC, CO, and NOx type emissions as well as fuel consumption. Scenarios are proposed which reflect the sensitivity of outputs to key input parameters. The findings of this research can be useful for future campus planning and the ideas can be extended to similar environments with traffic and parking problems such as business parks, corporate campuses, downtown districts, and special event venues.

Enterprises with a global supply network are at risk of lost revenue as a result of disruptive disasters at supplier locations. Various strategies exist for addressing this risk, and a variety of types of research has been done regarding the identification, assessment and response to the risk of disruption in a supply chain network. This thesis establishes a decision model to support Business Continuity Planning at the first-tier supplier level. The decision model incorporates discrete-event simulation of supply chain networks (through Simio software), Monte Carlo simulation, and risk index optimization. After modeling disruption vulnerability in a supply chain network, costs of implementing all combinations of Business Continuity Plans are ranked and then tested in discrete-event simulation for further insight into inventory levels, unmet customer demand, production loss and related costs. A case study demonstrates the implementation of the decision support process and tests a historical set of data from a large manufacturing company. Discrete-event simulation modeling of loss is confirmed to be accurate. The relevance of the model concept is upheld and recommendations for future work are made.

The purpose of this thesis is to model the effects of anti-price gouging (APG) legislation on the costs to businesses during the recovery period of a disaster. A system dynamics model of a business’s replenishment procedures is used to simulate the effects of APG legislation on business performance. Economists have published expansive research on the effects of price ceilings on supply and demand, but there is little research evidence on the operational consequences of price ceiling legislation on business costs. APG legislation increases consumer’s forward buying and shortage gaming after a disaster by removing price incentives to be frugal. Forward buying and shortage gaming are two key drivers of the demand variation and the bullwhip effect, which leads to increased inventory costs, misguided capacity expansion and reduced service levels. These costs have a negative impact on local businesses that are critical to a community’s economic health and recovery from a disaster. The simulation results from this thesis show that APG legislation is not an effective regulatory response to decrease the impact of disasters on affected communities.

The condition of multimorbidity — the presence of two or more medical conditions in an individual — is a growing phenomenon worldwide. In the United States, multimorbid patients represent more than a third of the population and the trend is steadily increasing in an already aging population. There is thus a pressing need to understand the patterns in which multimorbidity occurs, and to better understand the nature of the care that is required to be provided to such patients. In this thesis, we use data from the Medical Expenditure Panel Survey (MEPS) from the years 2011 to 2015 to identify combinations of multiple chronic conditions (MCCs). We first quantify the significant heterogeneity observed in these combinations and how often they are observed across the five years. Next, using two criteria associated with each combination -- (a) the annual prevalence and (b) the annual median expenditure -- along with the concept of non-dominated Pareto fronts, we determine the degree of impact each combination has on the healthcare system. Our analysis reveals that combinations of four or more conditions are often mixtures of diseases that belong to different clinically meaningful groupings such as the metabolic disorders (diabetes, hypertension, hyperlipidemia); musculoskeletal conditions (osteoarthritis, spondylosis, back problems etc.); respiratory disorders (asthma, COPD etc.); heart conditions (atherosclerosis, myocardial infarction); and mental health conditions (anxiety disorders, depression etc.). Next, we use unsupervised learning techniques such as association rule mining and hierarchical clustering to visually explore the strength of the relationships/associations between different conditions and condition groupings. This interactive framework allows epidemiologists and clinicians (in particular primary care physicians) to have a systematic approach to understand the relationships between conditions and build a strategy with regards to screening, diagnosis and treatment over a longer term, especially for individuals at risk for more complications. The findings from this study aim to create a foundation for future work where a more holistic view of multimorbidity is possible.

<p>This thesis deals with combinatorial and geometric aspects of linear optimization, and consists of two parts.</p> <p>In the first part, we address a conjecture formulated in 2008 and stating that the largest possible average diameter of a bounded cell of a simple hyperplane arrangement of n hyperplanes in dimension d is not greater than the dimension d. The average diameter is the sum of the diameters of each bounded cell divided by the total number of bounded cells, and then we consider the largest possible average diameter over all simple hyperplane arrangements. This quantity can be considered as an indication of the average complexity of simplex methods for linear optimization. Previous results in dimensions 2 and 3 suggested that a specific type of extensions, namely the covering extensions, of the cyclic arrangement might achieve the largest average diameter. We introduce a method for enumerating the covering extensions of an arrangement, and show that covering extensions of the cyclic arrangement are not always among the ones achieving the largest diameter.</p> <p>The software tool we have developed for oriented matroids computation is used to exhibit a counterexample to the hypothesized minimum number of external facets of a simple arrangement of n hyperplanes in dimension d; i.e. facets belonging to exactly one bounded cell of a simple arrangement. We determine the largest possible average diameter, and verify the conjectured upper bound, in dimensions 3 and 4 for arrangements defined by no more than 8 hyperplanes via the associated uniform oriented matroids formulation. In addition, these new results substantiate the hypothesis that the largest average diameter is achieved by an arrangement minimizing the number of external facets.</p> <p>The second part focuses on the colourful simplicial depth, i.e. the number of colourful simplices in a colourful point configuration. This question is closely related to the colourful linear programming problem. We show that any point in the convex hull of each of (d+1) sets of (d+1) points in general position in R^d is contained in at least (d+1)²/2 simplices with one vertex from each set. This improves the previously established lower bounds for d>=4 due to Barany in 1982, Deza et al in 2006, Barany and Matousek in 2007, and Stephen and Thomas in 2008.</p> <p>We also introduce the notion of octahedral system as a combinatorial generalization of the set of colourful simplices. Configurations of low colourful simplicial depth correspond to systems with small cardinalities. This construction is used to find lower bounds computationally for the minimum colourful simplicial depth of a configuration, and, for a relaxed version of the colourful depth, to provide a simple proof of minimality.</p> / Doctor of Philosophy (PhD)