The rise of market globalization creates challenges for business executives seeking to pursue foreign direct investment (FDI) in least developed countries (LDC), such as Afghanistan. Multinational corporate (MNC) executives need strategies that will improve the timely delivery of minerals for mining projects in LDCs. Guided by the force field analysis theory, the purpose of this holistic, single-case study was to explore the strategy that 5 MNC executives in Beijing, China, used to improve the timely delivery of minerals associated with the Aynak copper mine project in Afghanistan. Semistructured interviews were used to elicit detailed narratives from MNC executives about their experiences to develop strategies for mining projects in LDCs. A review of company documents, as well as member-checking of initial interview transcripts, helped to bolster the trustworthiness of final interpretations. Study results included 2 themes. Theme 1 was determinants of mine investment strategies in LDCs that included an exploration of driving forces, restraining forces, neutral forces, and the effect of those forces. Theme 2 was FDI strategies for copper mine projects in LDCs that included the comparison of cost leadership strategy, differentiation strategy, and combination of cost leadership and differentiation strategies. By implementing a cost leadership strategy and best practices, MNC executives were able to achieve greater success to improve timely delivery of minerals associated with FDI copper mine projects in LDCs. Social implications include ongoing efforts of Afghan government leaders to implement effective economic policies that decrease unemployment while reducing poverty.

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.

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.