We are living in an unjust world where the majority of humanity is subject to unequal economic and public policy systems that perpetuate cycles of poverty. Civil society, of which international NGOs are major players, are acknowledging the need for systemic, transformational change, which has to include meaningful participation in decision making processes by those whom are most vulnerable. In order to more effectively play this role, a number of international NGOs are moving decentralised structures (often comprised of independent entities at country level) to legitimately represent their primary constituents - those that are vulnerable, living in poverty, mostly in the global South. A consequence of this trajectory is added internal complexity and the creation of new management challenges, as decision making processes become more participatory and transparent. In addition, this new reality of complex federal structures also requires that power inequities between entities, are openly acknowledged and managed. Despite these challenges, this is a non-negotiable journey for many international NGOs and they acknowledge the need to adapt their management mechanisms to better handle this internal complexity. First-hand experience sparked the interest to apply operational research and system dynamics approaches to one such management mechanism, that of resource planning and allocation within international NGOs. This study aims to develop a set of insights, based on the system dynamics model, that could be useful to international NGO decision makers as they respond to their “real life” resource allocation challenges. Problem structuring methods are applied to these resource allocation challenges to gain a deeper understanding of the core components of resource allocation in order to develop a generic system dynamics model that simulates the necessary behaviours based on stakeholder input. A set of management scenarios are developed and form the basis for conducting experimental runs on the generic system dynamics model, testing different parameters in an effort to compare quantitative results. These quantitative results are used to compare performance against the original generic model, analysing trends and model behaviour to inform qualitative recommendations and conclusions.

The implementation of a fast track section is a commonly used strategy to improve patient flow in emergency departments (EDs). A fast track section reserves resources (beds, doctors and nurses) for lower acuity patients, and is thus aimed to reduce the wait time and length of stay of these patients. We use a discrete event simulation to investigate the impact of adding a fast track section to an emergency department. We quantify the effect of introducing a fast track on length of stay and waiting time to bed for low and high acuity patients in a crowded ED and compare it to an ED without fast track (Combined ED). We simulate a crowded ED by increasing the patient arrival rate, changing the acuity mix and increasing the time taken for admitted patients in the ED to obtain an inpatient bed (boarding time). We demonstrate that, when compared to a Combined ED with the same number of resources, the introduction of a fast track reduces the wait time to bed for lower acuity patients. However, this comes at the cost of increased waiting time for some higher acuity patients, which is unacceptable in practice. In investigating the solutions to this problem, we find that changing patient prioritization is the most effective way of reducing wait times under crowding. This change in priority does not require the addition of beds, doctors and nurses, and is therefore a cost-effective approach. Finally, we discuss the implications of our results for emergency departments.

An Optimization Approach to Employee Scheduling Using Fuzzy Logic William G. Spence Selection of sales employees is critical because the sales employees represent the company’s image, competitive advantage, technology, and values. In many service systems the majority of consumer contact is with the sales department. Since there are different types of customers, scheduling quality salespersons who can adequately help consumers may affect revenue. This thesis proposes a new methodology for the scheduling of employees in a service system. The methodology uses Fuzzy Logic to calculate possible sales and Linear Programming to create an optimal schedule. This approach enables the rating of sales employees with respect to three customer’s types (Lookie Lou, Price Shopper and Buyer). The salesperson rating, along with customer arrival distribution is then used to optimize sale person scheduling, with the objective of revenue maximization. The uniqueness of this thesis lies in the combination of Fuzzy Logic and Linear Programming. The combination of these two disciplines provides an adaptive tool that can be used to optimize employee scheduling based on personality traits.

This study aims to define workforce agility via a literature review and a conducted research survey of the Kennedy Space Center (KSC) Engineering workforce. The survey results will help us determine the workforce agility of the KSC engineering team and understand what has allowed them to transition from the Space Shuttle Program to the new Exploration Ground System (EGS) program. Structural Equation Modeling was used to develop a model of relationships to test the hypotheses. The results show that organizational practices and psychological empowerment significantly support workforce agility. The implications of this study for understanding the characteristics of workforce agility are also discussed.

A fixed charge model has been developed for a case study of a total energy power plant involving energy demands for chilled water, high temperature hot water, and electrical power. Using a FORTRAN computer program, which employs the Land and Doig branch and bound algorithm, the system model is solved for different sets of system demands for multiple energy users, and the following set of information is obtained to guide the decisions of operations personnel: 1. Recommend on/off status for each piece of equipment in the system. 2. Recommended rate of operation for each piece of machinery. 3. Required amounts of different types of fuel to satisfy system demands. 4. A preferred path of flow through the system for each type of energy purchased or produced within the system.

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An interactive information system has been developed for the College of Engineering to promote faster and more accurate trial advisement interviews between professors and the students they advice. This system also provides departmental administrative personnel with information to guide course scheduling. The system has the capability of providing the following information concerning undergraduate students enrolled in the College of Engineering: 1. A student's academic progress toward graduation. 2. A projection for four quarters of enrollments in engineering core courses. 3. The current status of any course offered as a requirement for obtaining a Bachelor's Degree in one of the Engineering disciplines. 4. The current status of any program leading to a Bachelor's Degree in any of the Engineering disciplines. The system provides the user with the additional capabilities to: 1. Update the database containing information pertaining to students enrolled in the College of Engineering. 2. Update the database containing information concerning courses offered as a requirement for obtaining a Bachelor's Degree in one of the Engineering disciplines. 3. Update the database containing information concerning the degree programs offered by the College of Engineering. The system is user oriented and operation does not require any knowledge of computer programming. Complete documentation is available to facilitate trouble-free user operation and programming any desirable additional capabilities in the future.

A linear programming model has been developed for a Central Energy Plant which minimizes operational costs for a system involving the generation of chilled water and high temperature hot water plus the generation and/or purchase of electric power with equipment using natural gas or fuel oil energy. The fundamental concepts developed herein are sufficient for the analysis of any combination of energy supplies, demands, and energy conversion equipment. Utilization of this model is demonstrated with a case study and computer program results for high and low temperature environments. The linear programming model approach establishes a well-defined framework for the analysis of complex utility systems and provides valuable results for the economical operation of a Central Energy Plant.

This thesis aims to forecast aircraft demand in the aerospace and defense industry, specifically aircraft orders and deliveries. Orders are often placed by airline companies with aircraft manufacturers, and then suddenly canceled due to changes in plans. Therefore, at some point during the three-year lead time, the number of orders placed and realized deliveries may be quite different. As a result, orders and deliveries are very difficult to predict and are influenced by many different factors. Among these factors are past trends, macroeconomic indicators as well as aircraft sales measures. These predictor variables were analyzed thoroughly, then used with time series and multiple regression forecasting methods to develop different forecasts for quarterly and annual orders and deliveries. The relative accuracies of forecasts were measured and compared through the use of Theil’s U statistic. Finally, a linear program was used to aggregate multiple forecasts to develop an optimal combination of all forecasts. In conclusion, the methods employed in this thesis are quite effective and produce a wholesome aggregate forecast with an error that is generally quite low for a forecasting task as challenging as this one.

<p>As energy costs and energy used by server farms increase, so does the desire to implement energy-aware policies. Although under some cost functions, optimal policies for single as well as multiple server systems are known, large gaps in theoretical knowledge are present in the field. Specifically, there exists many widely used and non-trivial cost functions, where the corresponding optimal policy remains unknown. This work presents and leverages a model which allows for the exact analysis of these optimal policies with considerable generality, for on/off single server systems under a broad range of cost functions that are based on expected response time, energy usage and switching costs. Furthermore, from the results derived in the analysis, several applications and implications are presented and discussed. This includes the determination of routing probabilities to show a range of non-trivial optimal routing probabilities and server configurations when energy concerns are a factor.</p> / Master of Applied Science (MASc)