Adaptive radiation therapy re-optimizes treatment plans based on updated tumor geometries from magnetic resonance imaging scans. However, the imaging process is costly in labor and equipment. In this study, we develop a mathematical model that describes tumor evolution based on a Markov assumption. We then extend the model to predict tumor evolution with any level of information from a new patient: weekly MRI scans are used to estimate transition probabilities when available, otherwise historical MRI scans are used. In the latter case, patients in the historical data are clustered into two groups, and the model relates the new patient's behavior to the existing two groups. The models are evaluated with 33 cervical cancer patients from Princess Margaret Cancer Centre. The result indicates that our models outperform the constant volume model, which replicates the current clinical practice.

In an emergency department (ED), information flow is of high value, as the ability to react quickly directly affects the patients’ well being. One of the gaps in the information flow is in the order tracking process. This paper focuses on modelling the feedback in this process from the order being issued until it has been fulfilled. We address this problem using discrete-event simulation. Additionally, we use the mathematical theory of communication to evaluate the information con- tent in the current and proposed systems. We perform computational tests on these models to compare their performance. Experimental results show that the problem can be effectively modelled using our approach and the effects of feedback on the physician decision-making can be better understood. The results indicate that additions of as little as one point of feedback have practically significant effects on the amount of time that an order spends in the system.

In this study, a user-centred design approach was used to develop a mobile health application designed to support adult T1DM patients with their self-management routine. In the requirements gathering phase, an observational study of a diabetes clinic and patient interviews were conducted. An analysis of the data collected from this phase helped identify the functional design requirements used to guide the design. Using a rapid prototyping approach, data visualizations, game-based elements, carb-counting and social networking features were explored. The final prototype developed in this research was evaluated for its ease of use and perceived usefulness. The design was found to be generally easy to use. With respect to data visualizations, participants preferred the scatter plot view of their blood glucose readings to a bar chart. In addition, it was found that all participants wanted a way to track their HbA1c on a regular basis.

The maintenance of equipment has been an important issue for companies for many years. For systems with hidden or unrevealed failures (i.e., failures are not self-announcing), a common practice is to regularly inspect the system looking for such failures. Examples of these systems include protective devices, emergency devices, standby units, underwater devices etc. If no periodical inspection is scheduled, and a hidden failure has already occurred, severe consequences may result. Research on periodical inspection seeks to establish the optimal inspection interval (Failure Finding Interval) of systems to maximize availability and/or minimize expected cost. Research also focuses on important system parameters such as unavailability. Most research in this area considers non-negligible downtime due to repair/replacement but ignores the downtime caused by inspections. In many situations, however, inspection time is non-negligible. We address this gap by proposing an optimal failure finding interval (FFI) considering both non-negligible inspection time and repair/replacement time. A novel feature of this work is the development of models for both age-based and calendar-based inspection policies with random/constant inspection time and random/constant repair/replacement time. More specifically, we first study instantaneous availability for constant inspection and repair/replacement times. We start with the assumption of renewal of the system at each inspection. We then consider models with the assumption of renewal only after failure. We also develop limiting average availability models for random inspection and repair/replacement times, considering both age-based and calendar-based inspection policies. We optimize these availability models to obtain an optimal FFI in order to maximize the system’s availability. Finally, we develop several cost models for both age-based and calendar-based inspection policies with random inspection time and repair/replacement time. We formulate the model for constant inspection time and repair/replacement time as a special case. We investigate the optimization of cost models for each case to obtain optimal FFI in order to minimize the expected cost. The numerical examples and case study presented in the dissertation demonstrate the importance of considering non-negligible downtime due to inspection.

All individuals are unique in their characteristics. As such, their positive and negative contributions to system performance differ. In any system that is not fully automated, the effect of the human participants has to be considered when one is interested in the performance optimization of the system. Humans are intelligent, adaptive, and learn over time. At the same time, humans are error-prone. Therefore, in situations where human and hardware have to interact and complement each other, the system faces advantages and disadvantages from the role the humans play. It is this role and its effect on performance that is the focus of this dissertation. When analyzing the role of people, one can focus on providing resources to enable the human participants to produce more. Alternatively, one can strive to ensure the occurrence of less frequent and impactful errors. The focus of the analysis in this dissertation is the latter. Our analysis can be categorized into two parts. In the first part of our analysis, we consider a short term planning horizon and focus directly on failure risk analysis. What can be done about the risk stemming from the human participant? Any proactive steps that can be taken will have the advantage of reducing risk, but will also have a cost associated with it. We develop a cost-benefit analysis to enable a decision-maker to choose the optimal course of action for revenue maximization. We proceed to use this model to calculate the minimum acceptable level of risk, and the associated skill level, to ensure system profitability. The models developed are applied to a case study that comes from a manufacturing company in Ontario, Canada. In the second part of our analysis, we consider a longer planning horizon and are focused on output maximization. Human learning, and its effect on output, is considered. In the first model we develop, we use learning curves and production forecasting models to optimally assign operators, in order to maximize system output. In the second model we develop, we perform a failure risk analysis in combination with learning curves, to forecast the total production of operators. Similar to the first part of our analysis, we apply the output maximization models to the aforementioned case study to better demonstrate the concepts.

In Canada and around the world, there has been an increased focus on the efficiency, cost and access to health care services. One area of particular focus is surgical procedures, often with government funding and policies focused on reducing wait times through pay for performance and volume target initiatives. In Ontario, an expert panel was assembled to evaluate the current state of surgical processes and provide recommendations to improve access, efficiency and quality. This thesis addresses the panel's recommendation for a simulation-based decision tool to help hospitals inform decisions that can lead to improved access and efficiency. A generalised, simulation based perioperative decision tool is presented that can be used to test a variety of tactical decisions. The generic model has been applied to six hospitals of varying sizes, ranging from large academic centres to small rural community hospitals. The model remains in use at some of the hospitals to regularly inform decisions. The model is also being applied to additional hospital sites. During application of the generic model, challenges in design decisions and validation were encountered. As a result, a series of principles are proposed to guide future generic modelling design and achieving user acceptance. These principles add to the generic simulation modelling and healthcare modelling research fields by laying some groundwork for a formalised approach to designing effective generic simulation models and achieving confidence in results. Finally, the research demonstrates two uses of the generic model: as decision tool and as a demonstrative tool. As a decision tool the model is used to compare numerous potential tactical decision options under consideration. As a demonstrative tool, the model is used to quantify the effect of poor practices on hospital performance. The design of the generic model only considers efficient processes and best practices. When model results are compared to historical performance, decision makers are able to quantify the effect of existing poor practices on their performance and decision making. The tool enables users to base their tactical level decisions on the assumption that good practices and procedures are followed.

Users have difficulty relying on automated combat identification aids; however, verbally informing users of the automation reliability has helped them rely on the automation more appropriately. A number of interfaces that displayed automation reliability information in real time were developed and tested. In Experiment I, participants used the interfaces in the IMMERSIVE simulation, a first person shooter game. The results showed that the form of the interface affected both reliance on the automation and sensitivity in discriminating hostile and friendly targets. The difference in sensitivity and reliance may be attributed to how participants allocated their attention among the displays. In Experiment II, still combat scenes were presented to the participants for 400 or 800 milliseconds (as opposed to 10 seconds in Experiment I) to place additional time stress on attention resources. The results replicated the results of Experiment I, but sensitivity measures showed a dependence on reliability of the automation.

Here we consider the verification and semantic integration for the set of first-order time ontologies by Allen-Hayes, Ladkin, and van Benthem that axiomatize time as points, intervals, or a combination of both within an ontology repository environment. Semantic integration of the set of time ontologies is explored via the notion of theory interpretations using an automated reasoner as part of the methodology. We use the notion of representation theorems for verification by characterizing the models of the ontology up to isomorphism and proving that they are equivalent to the intended structures for the ontology. Provided is a complete account of the meta-theoretic relationships between ontologies along with corrections to their axioms, translation definitions, proof of representation theorems, and a discussion of various issues such as class-quantified interpretations, the impact of namespacing support for Common Logic, and ontology repository support for semantic integration as related to the time ontologies examined.

The maintenance of equipment has been an important issue for companies for many years. For systems with hidden or unrevealed failures (i.e., failures are not self-announcing), a common practice is to regularly inspect the system looking for such failures. Examples of these systems include protective devices, emergency devices, standby units, underwater devices etc. If no periodical inspection is scheduled, and a hidden failure has already occurred, severe consequences may result. Research on periodical inspection seeks to establish the optimal inspection interval (Failure Finding Interval) of systems to maximize availability and/or minimize expected cost. Research also focuses on important system parameters such as unavailability. Most research in this area considers non-negligible downtime due to repair/replacement but ignores the downtime caused by inspections. In many situations, however, inspection time is non-negligible. We address this gap by proposing an optimal failure finding interval (FFI) considering both non-negligible inspection time and repair/replacement time. A novel feature of this work is the development of models for both age-based and calendar-based inspection policies with random/constant inspection time and random/constant repair/replacement time. More specifically, we first study instantaneous availability for constant inspection and repair/replacement times. We start with the assumption of renewal of the system at each inspection. We then consider models with the assumption of renewal only after failure. We also develop limiting average availability models for random inspection and repair/replacement times, considering both age-based and calendar-based inspection policies. We optimize these availability models to obtain an optimal FFI in order to maximize the system’s availability. Finally, we develop several cost models for both age-based and calendar-based inspection policies with random inspection time and repair/replacement time. We formulate the model for constant inspection time and repair/replacement time as a special case. We investigate the optimization of cost models for each case to obtain optimal FFI in order to minimize the expected cost. The numerical examples and case study presented in the dissertation demonstrate the importance of considering non-negligible downtime due to inspection.

Innovation diffusion models have been studied extensively to forecast and explain the adoption process for new products or services. These models are often formulated using one of two approaches: The first, and most common is a macro-level approach that aggregates much of the market behaviour. An advantage of this method is that forecasts and other analyses may be performed with the necessity of estimating few parameters. The second is a micro-level approach that aims to utilize microeconomic information pertaining to the potential market and the innovation. The advantage of this methodology is that analyses allow for a direct understanding of how potential customers view the innovation. Nevertheless, when individuals are making adoption decisions, the reality of the situation is that the process consists of at least two stages: First, a potential adopter must become aware of the innovation; and second the aware individual must decide to adopt. Researchers, have studied multi-stage diffusion processes in the past, however a majority of these works employ a macro-level approach to model market flows. As a result, a direct understanding of how individuals value the innovation is lacking, making it impossible to utilize this information to model realistic word-of-mouth behaviour and other network dynamics. Thus, we propose a two-stage integrated model that utilizes the benefits of both the macro- and micro-level approaches. In the first stage, potential customers become aware of the innovation, which requires no decision making by the individual. As a result, we employ a macro-level diffusion process to describe the first stage. However, in the second stage potential customers decide whether to adopt the innovation or not, and we utilize a micro-level methodology to model this. We further extend the application to include forward looking behaviour, heterogeneous adopters and segmented Bayesian learning, and utilize the adopter's satisfaction levels to describe biasing and word-of-mouth behaviour. We apply the proposed model to Canadian colour-TV data, and cross-validation results suggest that the new model has excellent predictive capabilities. We also apply the two-stage model to early U.S. hybrid-electric vehicle data and results provide insightful managerial observations.