A critical part of traffic safety is a driver’s ability to detect and respond to emergency roadway hazards. This thesis uses eye movements and motor responses to divide driver perception-response time in three stages: perception, inspection, and movement time. The effects of cognitive distraction and repeated exposure on each stage were investigated for three distinct hazards (left-turning vehicle, pedestrian, right-incursion vehicle). In general, there were varying effects of cognitive distraction observed depending on the hazard being responded to. Cognitive distraction resulted in a significant increase in perception times for the pedestrian and right-incursion vehicle hazards, whereas cognitive distraction resulted in significantly longer inspection times for the left-turning vehicle hazard. When considering the effect of repeated scenario exposure, perception times were the most greatly affected. Perception times were significantly shorter during the second exposure to the left-turning vehicle hazard in the baseline condition, and for all hazards in the distraction condition.

In emergency medical transport, “time to definite care” is very important. Emergency medical services and transport medicine agencies have several possible vehicle options for interfacility transfers. Use of a land vehicle, helicopter, or fixed wing aircraft will be dependent on patient condition, distance between sending and receiving hospitals, crew configuration and capabilities, and other factors such as weather and road conditions. This thesis lays out the complex process of patient transfers and highlights the challenges in decision making under time pressure; it then describes the behaviour of human operators in estimating time to definite care. To support the operators in choosing a transportation mode, a decision support tool was built, which provides relevant time estimates for interfacility transfers based on historical dispatch and call data. The goal is to enable operators to make evidence-based decisions on vehicle allocation. A prototype interface was generated and was evaluated through a usability study.

A critical part of traffic safety is a driver’s ability to detect and respond to emergency roadway hazards. This thesis uses eye movements and motor responses to divide driver perception-response time in three stages: perception, inspection, and movement time. The effects of cognitive distraction and repeated exposure on each stage were investigated for three distinct hazards (left-turning vehicle, pedestrian, right-incursion vehicle). In general, there were varying effects of cognitive distraction observed depending on the hazard being responded to. Cognitive distraction resulted in a significant increase in perception times for the pedestrian and right-incursion vehicle hazards, whereas cognitive distraction resulted in significantly longer inspection times for the left-turning vehicle hazard. When considering the effect of repeated scenario exposure, perception times were the most greatly affected. Perception times were significantly shorter during the second exposure to the left-turning vehicle hazard in the baseline condition, and for all hazards in the distraction condition.

In emergency medical transport, “time to definite care” is very important. Emergency medical services and transport medicine agencies have several possible vehicle options for interfacility transfers. Use of a land vehicle, helicopter, or fixed wing aircraft will be dependent on patient condition, distance between sending and receiving hospitals, crew configuration and capabilities, and other factors such as weather and road conditions. This thesis lays out the complex process of patient transfers and highlights the challenges in decision making under time pressure; it then describes the behaviour of human operators in estimating time to definite care. To support the operators in choosing a transportation mode, a decision support tool was built, which provides relevant time estimates for interfacility transfers based on historical dispatch and call data. The goal is to enable operators to make evidence-based decisions on vehicle allocation. A prototype interface was generated and was evaluated through a usability study.

Bayesian process control is a statistical process control (SPC) scheme that uses the posterior state probabilities as the control statistic. The key issue is to decide when to restore the process based on real-time observations. Such problems have been extensively studied in the framework of partially observable Markov decision processes (POMDP), with particular emphasis on the structure of optimal control policy. Almost all existing structural results on the optimal policies are limited to the two-state processes, where the class of control-limit policy is optimal. However, the two-state model is a gross simplification, as real production processes almost always involve multiple states. For example, a machine in the production system often has multiple failure modes differing in their effects; the deterioration process can often be divided into multiple stages with different degradation levels; the condition of a complex multi-unit system also requires a multi-state representation. We investigate the optimal control policies for multi-state processes with fixed sampling scheme, in which information about the process is represented by a belief vector within a high dimensional probability simplex. It is well known that obtaining structural results for such high-dimensional POMDP is challenging. Firstly, we prove that for an infinite-horizon process subject to multiple competing assignable causes, a so-called conditional control limit policy is optimal. The optimal policy divides the belief space into two individually connected regions, which have analytical bounds. Next, we address a finite-horizon process with at least one absorbing state and show that a structured optimal policy can be established by transforming the belief space into a polar coordinate system, where a so-called polar control limit policy is optimal. Our model is general enough to include many existing models in the literature as special cases. The structural results also lead to significantly efficient algorithms for computing the optimal policies. In addition, we characterize the condition for some out-of-control state to be more desirable than the in-control state. The existence of such counterintuitive situation indicates that multi-state process control is drastically different from the two-state case.

Emergency department (ED) overcrowding is a challenge faced by many hospitals. One approach to mitigate overcrowding is to anticipate high levels of overcrowding. The purpose of this study was to forecast a measure of ED overcrowding four hours in advance to allow clinicians to prepare for high levels of overcrowding. The chosen measure of ED overcrowding was ED length of stay compliance measures set by the Ontario government. A feed-forward artificial neural network (ANN) was designed to perform a time series forecast on the number of patients that were non-compliant. Using the ANN compared to historical averages, a 70% reduction in the root mean squared error was observed as well as good discriminatory ability of the ANN model with an area under the receiver operating characteristic curve of 0.804. Therefore, using ANNs to forecast ED overcrowding gives clinicians an opportunity to be proactive, rather than reactive, in ED overcrowding crises.

Emergency department (ED) overcrowding is a challenge faced by many hospitals. One approach to mitigate overcrowding is to anticipate high levels of overcrowding. The purpose of this study was to forecast a measure of ED overcrowding four hours in advance to allow clinicians to prepare for high levels of overcrowding. The chosen measure of ED overcrowding was ED length of stay compliance measures set by the Ontario government. A feed-forward artificial neural network (ANN) was designed to perform a time series forecast on the number of patients that were non-compliant. Using the ANN compared to historical averages, a 70% reduction in the root mean squared error was observed as well as good discriminatory ability of the ANN model with an area under the receiver operating characteristic curve of 0.804. Therefore, using ANNs to forecast ED overcrowding gives clinicians an opportunity to be proactive, rather than reactive, in ED overcrowding crises.

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.

Capacity pooling in production systems, in the form of production capacity or inventory pooling, has been extensively studied in the literature. While production capacity pooling has been proven to be beneficial, the impact of inventory pooling has been less significant. These results cannot be easily extended to repairable systems due to fundamental differences between repairable and production systems. For one thing, in repairable systems, the demand rate is a function of the number of operational machines, whereas it is exogenous and constant in production systems. In this Thesis, to serve different fleets of machines possibly at different locations, we study whether repair shop pooling is more cost effective than having dedicated on-site repair shops for each fleet. In the first model, we consider transportation delays and related costs, which have been traditionally ignored in the literature. We include on-site spare-part inventories that operate according to a continuous-review base-stock policy. Our numerical findings indicate that when transportation costs are reasonable, repair shop pooling is a better alternative. Next, we model a pooled repair shop that fixes failed components from different k-out-of-n:G systems. We permit a shared spare parts inventory serving all systems and/or reserved spare parts inventories for each system; we call this a hybrid model. The destination for a repaired component can be chosen either on a first-come-first-served basis or by following a static priority rule. Our findings show that both hybrid policies are more cost effective than having separate repair shops and inventories for each system. We propose implementing the multilevel rationing (MR) policy in systems with shared inventory. The MR policy prioritizes classes, and stops serving a class from inventory if the inventory level is below the inventory threshold identified for that class. When there is no inventory, the repaired component is sent to the highest priority class among those with down machines. To approximate the cost of the MR policy, we study an M/G/1//N queueing system serving multiple classes of customers with an unreliable server. Our numerical findings indicate that the MR policy performs as well as the epsilon-optimal policy and outperforms the hybrid policies.

Much study is required on how human behaviour affects resource consumption. Not only are Human Factors Engineers well equipped to study how to shape human behaviour, but contributors to conservation literature have asked for their involvement in the domain of energy conservation. This study took a novel, systematic, human-factors approach to providing feedback on energy consumption by testing the effects of providing three levels of feedback on conservation performance. The results showed that providing physical, functional, and task-based information aided performance more than physical information alone, but no more than providing physical and functional information together. More research is required to determine if physical and task information alone could achieve the same result, if study results would differ when two opposing task goals were given, and if the varying levels of feedback have a greater effect on novices.