In response to growing concerns over the susceptibility of water distribution systems to contamination, this thesis presents the design and evaluation of an ecological display for an intelligent water monitoring system. Work Domain Analysis and Control Task Analysis were used to uncover the information requirements of the display. The work domain model in this thesis is one of a limited number of such models to include a representation of the automation. The resulting ecological display was evaluated in a comparative usability study against an alternative display based on a competitor’s product. Nine certified water operators participated. The displays were compared across three categories of usability measures: effectiveness, efficiency, and satisfaction. No significant differences were observed. The results, however, helped identify design changes for the display. The implications for the design of intelligent automation displays, and the challenge of designing displays in parallel with early development of automation systems, are discussed.

A margin account is a type of brokerage account that allows investors to buy and sell financial securities using credit. The account’s margin requirement is the amount of collateral required, from the investor, to cover the funds or securities extended by the broker to the investor. In Canada, the primary driver of an account’s margin requirement is the account’s Capital Charge [CC] which is calculated using a set of regulatory rules. The regulations are degenerate in that hundreds of valid CCs often exist for a single account. This work outlines a linear optimization model for selecting the minimal CC out of the set of valid CCs for a given margin account. The method proposed is consistent with all of the regulatory requirements and is guaranteed optimal in most cases. Relative to existing methods, the new method produced an average CC reduction of approximately 2% and displayed qualitatively better run-times.

In 2008 Ontario implemented a screening program for colorectal cancer, which drew attention to the increasing demand for colonoscopies in the province. This trend and the forecasted demand of the new screening program created a need to increase capacity in hospital endoscopy suites. This thesis addresses this need by investigating throughput gains from scheduling according to physician specific procedure durations in endoscopy suites. This is accomplished through the development of a scheduler and a generic discrete event simulation. Case study results show that physician specific scheduling can increase throughput in the endoscopy suite while reducing undertime and only slightly increasing overtime. They further indicate the trade off between a 1:2 and 1:1 physician to room ratio, finding that while a 1:1 ratio increases throughput by 33% over a 1:2 ratio, physicians are 1.5 times more productive under a 1:2 ratio.

Despite years of research in residential energy conservation, means of inducing conservation behaviour through feedback are not well understood. In this thesis I take a novel approach to feedback design by addressing temporal inconsistencies that may hinder individuals from forming an intention to conserve. To help understand conservation compliance strategies, I proposed a visual framework to categorize interventions. I present two design heuristics that were inspired by temporal construal theory (Liberman & Trope, 2003). They were the impetus for the design of three feedback display prototypes, which were examined. Due to methodological limitations, significant improvements to compliance were not found. However, evidence suggests that comparative feedback may have supported reasoning about conservation rather than supporting conservation compliance directly. Future work includes refinement of feedback displays to avoid direct comparisons, exploring the use of nature imagery, and the study of a possible interaction between environmental values and comparative feedback on compliance.

This work focuses on the presentation of a methodology for the development and verification of expressive ontologies. Motivated by experiences with the development of first-order logic ontologies, we call attention to the inadequacies of existing development methodologies for expressive ontologies. We attempt to incorporate pragmatic considerations inspired by our experiences while maintaining the rigorous definition and verification of requirements necessary for the development of expressive ontologies. We leverage automated reasoning tools to enable semiautomatic verification of requirements, and to assist other aspects of development where possible. In addition, we discuss the related issue of ontology quality, and formulate a set of requirements for MACLEOD - a proposed development tool that would support our lifecycle.

Real options analysis has been recommended to identify and quantify opportunities where managerial flexibility can influence worth. However, real options models in the literature have become increasingly sophisticated, and managers have cited their reluctance to use such models due to their level of complexity and lack of transparency. Presented in this thesis is a real options model that can be easily incorporated into the current project selection methodology of a firm; the model uses managerial cash flow estimates to price real options on tangible investment opportunities in a financially consistent manner. Next, to demonstrate the application of real options analysis in practice, five real options models, including the proposed model, are applied to value a medical device project. The models all price the real option differently, due to the differences in their underlying assumptions, but they all yield the same investment conclusion: the medical device project has value.

Linear assets are among the largest and most important engineered systems; their reliability is of the utmost importance. This thesis presents an overview of the reliability estimation methods used for the various types of linear assets, both observation- and statistically-based. While observation-based reliability monitoring and estimation methods are necessarily particular to a certain type of asset, statistically-based methods developed for one type can potentially inform those used for another. Therefore, this thesis looks to point out commonalities in the methods for the statistical evaluation of the reliability of various types of linear assets, develop and extend reliability models and methods with this knowledge, and suggest how maintenance strategies may be improved. To help illustrate and test the models described in this paper a case study was conducted with a utility operator; this thesis shows the modelling results from the study, and demonstrates the model’s use in a maintenance decision model.

This thesis investigates the effect of a feedback-reward system on speeding and tailgating behaviours. Data utilized in this study were collected from 37 participants through a field trial commissioned by Transport Canada. In this trial, a feedback-reward system was investigated, which provided feedback and rewards to the drivers based on speed limit compliance and safe headway maintenance. The trial consisted of three phases: baseline, intervention, and post-intervention. During the intervention phase, real-time feedback was provided on an in-vehicle display. Participants also accumulated reward points and could view related information on a website. Results indicate that the intervention resulted in a significant increase in speed limit compliance, and this positive effect, although dampened, was still apparent after system removal. Similarly, results on headway compliance rate indicate a positive intervention effect, however, this effect did not sustain after system removal. These findings have implications for developing better aids to improve driving behaviour.

This research is an effort to improve the performance of successive approximation algorithm with a prime aim of solving finite states and actions, infinite horizon, stationary, discrete and discounted Markov Decision Processes (MDPs). Successive approximation is a simple and commonly used method to solve MDPs. Successive approximation often appears to be intractable for solving large scale MDPs due to its computational complexity. Action elimination, one of the techniques used to accelerate solving MDPs, reduces the problem size through identifying and eliminating sub-optimal actions. In some cases successive approximation is terminated when all actions but one per state are eliminated. The bounds on value functions are the key element in action elimination. New terms (action gain, action relative gain and action cumulative relative gain) were introduced to construct tighter bounds on the value functions and to propose an improved action elimination algorithm. When span semi-norm is used, we show numerically that the actual convergence of successive approximation is faster than the known theoretical rate. The absence of easy-to-compute bounds on the actual convergence rate motivated the current research to try a heuristic action elimination algorithm. The heuristic utilizes an estimated convergence rate in the span semi-norm to speed up action elimination. The algorithm demonstrated exceptional performance in terms of solution optimality and savings in computational time. Certain types of structured Markov processes are known to have monotone optimal policy. Two special action elimination algorithms are proposed in this research to accelerate successive approximation for these types of MDPs. The first algorithm uses the state space partitioning and prioritize iterate values updating in a way that maximizes temporary elimination of sub-optimal actions based on the policy monotonicity. The second algorithm is an improved version that includes permanent action elimination to improve the performance of the algorithm. The performance of the proposed algorithms are assessed and compared to that of other algorithms. The proposed algorithms demonstrated outstanding performance in terms of number of iterations and omputational time to converge.

For decades, reliability engineering techniques have been successfully applied in many industries to improve the performance of equipment maintenance management. Numerous inspection and optimization models are developed and widely used to achieve maintenance excellence, i.e. the balance of performance, risk, resources and cost to reach to an optimal solution. However, the application of all these techniques and models to medical devices is new. Hospitals, due to possessing a large number of difference devices, can benefit significantly if the optimization techniques are used properly in the equipment management processes. Most research in the area of reliability engineering for medical equipment mainly considers the devices in their design or manufacturing stage and suggests some techniques to improve the reliability. To this point, best maintenance strategies for medical equipment in their operating context have not been considered. We aim to address this gap and propose methods to improve current maintenance strategies in the healthcare industry. More specifically, we first identify or propose the criteria which are important to assess the criticality of medical devices, and propose a model for the prioritization of medical equipment for maintenance decisions. The model is a novel application of multi-criteria decision making methodology to prioritize medical devices in a hospital according to their criticality. The devices with high level of criticality should be included in the hospital’s maintenance management program. Then, we propose a method to statistically analyze maintenance data for complex medical devices with censoring and missing information. We present a classification of failure types and establish policies for analyzing data at different levels of the device. Moreover, a new method for trend analysis of censored failure data is proposed. A novel feature of this work is that it considers dependent failure histories which are censored by inspection intervals. Trend analysis of this type of data has not been discussed in the literature. Finally, we introduce some assumptions based on the results of the analysis, and develop several new models to find the optimal inspection interval for a system subject to hard and soft failures. Hard failures are instantaneously revealed and fixed. Soft failures are only rectified at inspections. They do not halt the system, although they reduce its performance or productivity. The models are constructed for two main cases with the assumption of periodic inspections, and periodic and opportunistic inspections, respectively. All numerical examples and case studies presented in the dissertation are adapted from the maintenance data received from a Canadian hospital.