Radiotherapy is one method of treating di erent diseases like cancer. It requires a treatment plan that clearly delineates target and non-target volumes, and the beams and their intensities to deliver the prescribed dose. Historical treatment plans often contain volume names that are unaccounted for. An approach is applied where desired volumes are detected and renamed to conform to current search standards. The mapped names provide an avenue for searching historical plans when performing outcomes analysis in the future to help improve quality in radiation therapy. A specific form known as intensity modulated radiation therapy is applied to total marrow irradiation, a method to remove all marrow in the body prior to bone marrow transplant. A set-covering approach is used, solved using heuristics and commercial packages to compare outcomes. Constraint programming is used in an attempt to better and to improve on the heuristic solutions.

The goal of this thesis was to explore the usability of on-foot sensors for ambulatory gait analysis. Using a human factors engineering approach, two different ambulatory gait analysis prototypes were assessed in two separate studies. The first study was conducted with student participants in a university setting and the second study was conducted in-situ with neurorehabilitation patients at Bridgepoint Hospital. The usability and patient experience of the prototypes were assessed, and based on these findings a set of recommendations was developed. These issues and recommendations are detailed in this thesis and are expected to inform the design of future iterations of the prototypes.

After formulating the problem and reviewing relevant research literature, a study was performed that compared reverse engineering with traditional scenario-based techniques in usability evaluation. In this case study, an online tool for creating asthma action plans was created and evaluated through questionnaires and focus groups. The tool was then tested in a controlled study using both a traditional scenario-based approach and the reverse engineering method. A group of twelve users built asthma action plans using each method in a randomized order. Results concerning usability, efficiency and the types of usability problems found were reported, along with recommendations for further research in the use of reverse engineering as a method of usability evaluation.

Two on-line task re-allocation methodologies capable of re-allocating agents to tasks on-line for minimum task completion time in dynamic environments are presented herein. The first methodology, the Dynamic Nearest Neighbour (DNN) Policy, is proposed for the operation of a fleet of vehicles in a city-like application of the dial-a-ride problem. The second methodology, the Dynamic Re-Pairing Methodology (DRPM) is proposed for the interception of a group of mobile targets by a dynamic team of robotic pursuers, where the targets are assumed to be highly maneuverable with a priori unknown, but real-time trackable, motion trajectories. Extensive simulations and experiments have verified the DNN policy to be tangibly superior to the first-come-first-served and nearest neighbour policies in minimizing customer mean system time, and the DRPM to be tangibly efficient in the optimal dynamic re-pairing of multiple mobile pursuers to multiple mobile targets for minimum total interception time.

Radiotherapy is one method of treating di erent diseases like cancer. It requires a treatment plan that clearly delineates target and non-target volumes, and the beams and their intensities to deliver the prescribed dose. Historical treatment plans often contain volume names that are unaccounted for. An approach is applied where desired volumes are detected and renamed to conform to current search standards. The mapped names provide an avenue for searching historical plans when performing outcomes analysis in the future to help improve quality in radiation therapy. A specific form known as intensity modulated radiation therapy is applied to total marrow irradiation, a method to remove all marrow in the body prior to bone marrow transplant. A set-covering approach is used, solved using heuristics and commercial packages to compare outcomes. Constraint programming is used in an attempt to better and to improve on the heuristic solutions.

The goal of this thesis was to explore the usability of on-foot sensors for ambulatory gait analysis. Using a human factors engineering approach, two different ambulatory gait analysis prototypes were assessed in two separate studies. The first study was conducted with student participants in a university setting and the second study was conducted in-situ with neurorehabilitation patients at Bridgepoint Hospital. The usability and patient experience of the prototypes were assessed, and based on these findings a set of recommendations was developed. These issues and recommendations are detailed in this thesis and are expected to inform the design of future iterations of the prototypes.

Fault detection and diagnosis of gear transmission systems have attracted considerable attention in recent years, due to the need to decrease the downtime on production machinery and to reduce the extent of the secondary damage caused by failures. However, little research has been done to develop gear shaft and planetary gear crack detection methods based on vibration signal analysis. In this thesis, an approach to gear shaft and planetary gear fault detection based on the application of the wavelet transform to both the time synchronously averaged (TSA) signal and residual signal is presented. Wavelet approaches themselves are sometimes inefficient for picking up the fault signal characteristic under the presence of strong noise. In this thesis, the autocovariance of maximal energy wavelet coefficients is first proposed to evaluate the gear shaft and planetary gear fault advancement quantitatively. For a comparison, the advantages and disadvantages of some approaches such as using variance, kurtosis, the application of the Kolmogorov-Smirnov test (K-S test), root mean square (RMS) , and crest factor as fault indicators with continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for residual signal, are discussed. It is demonstrated using real vibration data that the early faults in gear shafts and planetary gear can be detected and identified successfully using wavelet transforms combined with the approaches mentioned above. In the second part of the thesis, the planetary gear deterioration process from the new condition to failure is modeled as a continuous time homogeneous Markov process with three states: good, warning, and breakdown. The observation process is represented by two characteristics: variance and RMS based on the analysis of autocovariance of DWT applied to the TSA signal obtained from planetary gear vibration data. The hidden Markov model parameters are estimated by maximizing the pseudo likelihood function using the EM iterative algorithm. Then, a multivariate Bayesian control chart is applied for fault detection. It can be seen from the numerical results that the Bayesian chart performs better than the traditional Chi-square chart.

Nurses and physicians in General Internal Medicine (GIM) work in a complex environment where patients present with complex (co)morbidities; management of such patients requires input from a full range of medical disciplines. In addition, there is regular resident physician changeover every 8-9 weeks in this teaching environment, and patient “flow” problems caused by overcrowding and placement issues. This complexity causes difficulties in the information exchange between nurses and physicians necessary to manage patient care. Multidisciplinary team meetings have been suggested as helpful to interprofessional communication, and in General Internal Medicine these take place in the form of daily “Bullet Rounds”. More recently the use of process engineering approaches has been suggested as a way to increase efficiency in healthcare; this dissertation evaluates its impact on communication between nurses and physicians. The initial observational field study showed that information exchange was the main focus of dialogue in Bullet Rounds, and identified information gaps between nurses and physicians. Script Theory (Schank and Abelson 1977) was used to propose that information gaps in Bullet Rounds are caused by different knowledge and goals, which result in inconsistent scripts. A process engineering intervention took place in General Internal Medicine. Process engineering methods have been proposed as being helpful in process design and improvement in healthcare but have not been systematically evaluated. The researcher conducted a pre and post intervention study of Bullet Rounds in order to identify and analyse the impacts of a process engineering intervention on information exchange between nurses and physicians. The results showed that information loss decreased after the intervention but that resident physicians were not satisfied with the nurses-physician information exchange. The staff and resident physicians appeared to have distinct and different information needs and perspectives, while the nurses felt that the Bullet Rounds process had improved, but that it needed revisiting, and were not aligned with staff physicians on respective roles and responsibilities. The overall results suggest that even after the process engineering intervention, there was still misalignment of goals and scripts between the two groups of physicians and between the physicians and nurses, and strategies for addressing these gaps are proposed.

This thesis studies a single item periodic review inventory problem with stochastic demand, random price and quotation cost. It differs from the traditional inventory model in that at the beginning of each period, a decision is made whether to pay the quotation cost to get the price information. If it is decided to request a price quote then the next decision is on how many units to order; otherwise, there will be no order. An (r, S1, S2) policy with r < S2, S1 <= S2 is proposed for the problem with two prices. It prescribes that when the inventory is less than or equal to r, the price quotation is requested; if the higher price is quoted, then order up to S1, otherwise to S2. There are two cases, r < S1 or S1 <= r. In the first case, every time the price is quoted, an order is placed. It is a single reorder point two order-up-to levels policy that can be considered as an extension of the (s, S) policy. In the second case, S1 <= r, it is possible to “request a quote but not buy” if the quoted price is not favorable when the inventory is between S1 and r. Two total cost functions are derived for the cases r < S1 <= S2 and S1 <= r < S2 respectively. Then optimization algorithms are devised based on the properties of the cost functions and tested in numerical study. The algorithms successfully find the optimal policies in all of the 135 test cases. Compared to the exhaustive search, the running time of the optimization algorithm is reduced significantly. The numerical study shows that the optimal (r, S1, S2) policy can save up to 50% by ordering up to different levels for different prices, compared to the optimal (s, S) policy. It also reveals that in some cases it is optimal to search price speculatively, that is with S1 < r, to request a quote but only place an order when the lower price is realized, when the inventory level is between S1 and r.

Over the last year or so, we have witnessed the global effects and repercussions related to the field of finance. Supposed blue chip stocks and well-established companies have folded and filed for bankruptcy, an event that might have thought to been absurd two years ago. In addition, finance and investment science has grown over the past few decades to include a plethora of investment options and regulations. Now more than ever, developments in the field are carefully examined and researched by potential investors. This thesis involves an investigation and quantitative analysis of key money management problems. The primary area of interest is Portfolio Selection, where we develop advanced financial models that are designed for investment problems of the 21st century. Portfolio selection is the process involved in making large investment decisions to generate a collection of assets. Over the years the selection process has evolved dramatically. Current portfolio problems involve a complex, yet realistic set of managing constraints that are coupled to general historic risk and return models. We identify three well-known portfolio problems and add an array of practical managing constraints that form three different types of Mixed-Integer Programs. The product is advanced mathematical models related to risk-return portfolios, index tracking portfolios, and an integrated stock-bond portfolio selection model. The numerous sources of uncertainty are captured in a Stochastic Programming framework, and Goal Programming techniques are used to facilitate various portfolio goals. The designs require the consideration of modelling elements and variables with respect to problem solvability. We minimize trade-offs in modelling and solvability issues found in the literature by developing problem specific algorithms. The algorithms are tailored to each portfolio design and involve decompositions and heuristics that improve solution speed and quality. The result is the generation of portfolios that have intriguing financial outcomes and perform well with respect to the market. Portfolio selection is as dynamic and complex as the recent economic situation. In this thesis we present and further develop the mathematical concepts related to portfolio construction. We investigate the key financial problems mentioned above, and through quantitative financial modelling and computational implementations we introduce current approaches and advancements in field of Portfolio Optimization.