In this thesis, we study the repair shop scheduling problem(repair-to-stock) and the production/inventory system pricing and production scheduling problem(make-to-stock). For both types of problems, we compare the performance of different scheduling policies. For the make-to-stock type problem, we also study the performance of different pricing strategies. The optimal repair/production scheduling policy of both problems is difficult to characterize, and, therefore, is only formulated as a Markov Decision Process to numerically compute the optimal cost/profit. As an alternative, we propose the dynamic Myopic policy, which is easy to implement. The numerical study we have conducted demonstrates that the performance of Myopic policy is superior compared to the alternative policies and yields costs very close to the optimal for the repair-to-stock type problem. On the other hand, for the make-to-stock type problems, the performance of Myopic policy is not superior compared to the alternative policies when dynamic pricing strategy is implemented.

This thesis investigates the associations between crash outcomes, the existence and type of driver distraction as well as driver’s age. The crash outcomes considered in this thesis consist of the type of crash as well as the injury severity sustained by occupants involved in the crash. An ordered logit model was built to predict the likelihood of severe injuries and a multinomial model was developed to predict the likelihood that a driver will be involved in one of three common crash types: singular, angular, and rearend. In these models, various factors (e.g., weather, driver’s gender, and speeding) have been statistically controlled for, but the main focus was on the interaction of driver’s age and distraction type. The findings of this thesis have implications for policy making and prioritizing capabilities of distraction-related safety systems.

The purpose of this dissertation was to investigate the effects of the presence and magnitude of sensor noise on operators’ performance and control strategies using an Ecological Interface Design (EID) interface and a Single-Sensor Single-Indicator (SSSI) interface. To assist in the study of this topic, concepts from sensor technology, cognitive psychology, and cognitive engineering were utilized. Three studies were conducted using different types of sensor noise perturbations with DURESS III, a representative thermal-hydraulic process simulation: 1) global random increases in sensor noise magnitude, 2) global gradual increases in sensor noise magnitude, and 3) local gradual increases in sensor noise magnitude. Three displays (P, P+S, and P+F) were used in the studies, motivated by different interface design principles. There were four main findings. First, the EID condition performed significantly better than the SSSI conditions when sensor noise was set to an industry average level. Second, the robustness of the EID interface was compromised by global and large increases in sensor noise magnitude, but no more than the SSSI interface. Third, increasing the magnitude of sensor noise in selected low-level sensors had an impact on the performance and control stability of the EID condition, but no more than the SSSI condition. Fourth, in all three studies, the introduction of uncertainty in the form of sensor noise to both EID and SSSI interfaces forced participants to explore different control strategies. A number of contributions resulted from this research. First, this was the first set of studies to use the DURESS III microworld to investigate the impact of sensor noise on performance and control strategies. Second, this is the first piece of research to empirically assess the impact of different sensor noise magnitudes on the robustness of an EID interface. Third, this dissertation was the first to empirically investigate issues related to increases in sensor noise magnitude to local low-level sensors and their derivations to emergent features. Fourth, these studies constitute the first investigation of changes in control strategies in the context of increases in sensor noise magnitude. The findings are believed to be important for the applicability of EID in industrial settings.

Uncertainty is inherent in complex socio-technical systems such as in aviation, military, and surface transportation domains. An improved understanding of how operators comprehend this uncertainty is critical to the development of operations and technology. Towards the development of a model of time of arrival (TOA) uncertainty, Experiment 1 was conducted to determine how air traffic controllers estimate TOA uncertainty and to identify sources of TOA uncertainty. The resulting model proposed that operators first develop a library of speed and TOA profiles through experience. As they encounter subsequent aircraft, they compare each vehicle’s speed profile to their personal library and apply the associated estimate of TOA uncertainty. To test this model, a normative model was adopted to compare inferences made by human observers to the corresponding inferences that would be made by an optimal observer who had knowledge of the underlying distribution. An experimental platform was developed and implemented in which subjects observed vehicles with variable speeds and then estimated the mean and interval that captured 95% of the speeds and TOAs. Experiments 2 and 3 were then conducted and revealed that subjects overestimated TOA intervals for fast stimuli and underestimated TOA intervals for slow stimuli, particularly when speed variability was high. Subjects underestimated the amount of positive skew of the TOA distribution, particularly in slow/high variability conditions. Experiment 3 also demonstrated that subjects overestimated TOA uncertainty for short distances and underestimated TOA uncertainty for long distances. It was shown that subjects applied a representative heuristic by selecting the trained speed profile that was most similar to the observed vehicle’s profile, and applying the TOA uncertainty estimate of that trained profile. Multiple regression analyses revealed that the task of TOA uncertainty estimation contributed the most to TOA uncertainty estimation error as compared to the tasks of building accurate speed models and identifying the appropriate speed model to apply to a stimulus. Two systematic biases that account for the observed TOA uncertainty estimation errors were revealed: Assumption of symmetry and aversion to extremes. Operational implications in terms of safety and efficiency for the aviation domain are discussed.

The current research sets out to demonstrate a comprehensive approach to validate complex human performance models as applied to time-sensitive tasks. This document is divided into 4 sections. Section 1 (Chapters 1 – 3) outlines previous efforts in the literature that have attempted to validate complex human performance models in the field with an emphasis on manual control models, task network models, cognitive models and integrated architectures. Section 2 (Chapters 4 – 7) elaborates on a validation approach and applies it to a baseline model of a complex task in the air traffic control domain. Section 3 (Chapters 7-12) outlines the importance of adopting an iterative model development-model validation process and reports on the three model iterations in an attempt to improve the validity of the baseline model. Each model augmentation was validated using the same validation approach and measures that were defined in Section 2. Section 4 (Chapters 13-14) provides a discussion and interpretation of the model results and highlights contributions to the field of both model validation and the field of human performance modelling of complex systems.

A North American man has approximately a one in six chance of being diagnosed with prostate cancer in his lifetime. In most cases, there is no clearly optimal treatment, so he may be invited to participate in a treatment decision between several medically reasonable options, each with potential short- and long-term side effects. Information needs are high at diagnosis and can continue to be elevated for years or decades. Many men and their families seek information online, where, due partly to the array of websites available and high variation in information preferences, it can be difficult to find personally relevant and useful websites. This research sought to address this issue by developing methods to categorize prostate cancer websites and exploring quantitative and qualitative relationships between websites, information-seekers, and individuals’ assessments of websites. The research involved a series of three studies. In the first study, 29 men with prostate cancer participated in a needs assessment involving questionnaires, an interview, and interaction with a prototype website. In the second study, a detailed classification system was developed and applied to a set of forty websites selected to be representative of the variety of prostate cancer websites available. The third (online) study collected clinical, cognitive, and psychosocial details from 65 participants along with their ratings of websites from study two. A number of hypotheses were tested. One finding was that, compared to men with greater trust, men with lower trust in their physician tended to judge commercial websites as less relevant and useful, and found websites with descriptions of personal experiences more relevant and useful. Analyses also addressed a number of exploratory questions, including whether website and individual attributes might predict preferences for websites. Using discriminant analysis on 80% of the data, two functions were identified that predicted ratings significantly better than chance. These relationships were then validated with 20% of the data held back for testing. The results are discussed in terms of their implications for information tailoring and recommender systems for prostate cancer patients searching for information online. Limitations of the current research and recommendations for future research are also presented.

The purpose of this dissertation was to investigate the effects of the presence and magnitude of sensor noise on operators’ performance and control strategies using an Ecological Interface Design (EID) interface and a Single-Sensor Single-Indicator (SSSI) interface. To assist in the study of this topic, concepts from sensor technology, cognitive psychology, and cognitive engineering were utilized. Three studies were conducted using different types of sensor noise perturbations with DURESS III, a representative thermal-hydraulic process simulation: 1) global random increases in sensor noise magnitude, 2) global gradual increases in sensor noise magnitude, and 3) local gradual increases in sensor noise magnitude. Three displays (P, P+S, and P+F) were used in the studies, motivated by different interface design principles. There were four main findings. First, the EID condition performed significantly better than the SSSI conditions when sensor noise was set to an industry average level. Second, the robustness of the EID interface was compromised by global and large increases in sensor noise magnitude, but no more than the SSSI interface. Third, increasing the magnitude of sensor noise in selected low-level sensors had an impact on the performance and control stability of the EID condition, but no more than the SSSI condition. Fourth, in all three studies, the introduction of uncertainty in the form of sensor noise to both EID and SSSI interfaces forced participants to explore different control strategies. A number of contributions resulted from this research. First, this was the first set of studies to use the DURESS III microworld to investigate the impact of sensor noise on performance and control strategies. Second, this is the first piece of research to empirically assess the impact of different sensor noise magnitudes on the robustness of an EID interface. Third, this dissertation was the first to empirically investigate issues related to increases in sensor noise magnitude to local low-level sensors and their derivations to emergent features. Fourth, these studies constitute the first investigation of changes in control strategies in the context of increases in sensor noise magnitude. The findings are believed to be important for the applicability of EID in industrial settings.

Biomimetic design uses biological analogies to produce innovative engineering solutions. However, designers face challenges in identifying useful biological analogies and correctly applying the analogies identified to design solutions. To overcome these challenges, this thesis proposes the use of causally related functions in biomimetic design. Causally related functions describe how a desired function is enabled by another function. To support the use of causally related functions, a set of tools was developed. First, the causal relation template and mapping techniques (one-to-one mapping instructions and problem-independent scenario mapping) were devised to assist designers to identify and apply causally related functions from descriptions of biological phenomena. In pen-and-paper experiments with senior undergraduate engineering students, the causal relation template, if used correctly, facilitated the development of design concepts that were analogous to biological phenomena provided as sources of analogy. In addition, the mapping techniques reduced the percentage of participants who made non-analogous associations from biological phenomena to develop design concepts. Another tool developed was the causal relation retrieval method. The method uses syntactic information in natural language sentences to explicitly identify causally related functions. A modified verbal protocol study with graduate engineering students revealed that the retrieval method increased the likelihood of locating biological phenomena relevant to given design problems compared to a single verb-keyword search method. Also, the search matches located with the retrieval method were more likely to facilitate functional association to develop design concepts. These results demonstrate that the knowledge structure of causally related functions can support both the identification of relevant biological phenomena in natural language text and use of analogical reasoning between the biological phenomena and design solutions. The causal relation template and mapping strategies developed contribute to the field of biomimetic design as training methods for designers; and the causal relation retrieval method could serve a technique to bridge the gap between the natural language approach and the modeling approach to biomimetic design.

Biomimetic design uses biological analogies to produce innovative engineering solutions. However, designers face challenges in identifying useful biological analogies and correctly applying the analogies identified to design solutions. To overcome these challenges, this thesis proposes the use of causally related functions in biomimetic design. Causally related functions describe how a desired function is enabled by another function. To support the use of causally related functions, a set of tools was developed. First, the causal relation template and mapping techniques (one-to-one mapping instructions and problem-independent scenario mapping) were devised to assist designers to identify and apply causally related functions from descriptions of biological phenomena. In pen-and-paper experiments with senior undergraduate engineering students, the causal relation template, if used correctly, facilitated the development of design concepts that were analogous to biological phenomena provided as sources of analogy. In addition, the mapping techniques reduced the percentage of participants who made non-analogous associations from biological phenomena to develop design concepts. Another tool developed was the causal relation retrieval method. The method uses syntactic information in natural language sentences to explicitly identify causally related functions. A modified verbal protocol study with graduate engineering students revealed that the retrieval method increased the likelihood of locating biological phenomena relevant to given design problems compared to a single verb-keyword search method. Also, the search matches located with the retrieval method were more likely to facilitate functional association to develop design concepts. These results demonstrate that the knowledge structure of causally related functions can support both the identification of relevant biological phenomena in natural language text and use of analogical reasoning between the biological phenomena and design solutions. The causal relation template and mapping strategies developed contribute to the field of biomimetic design as training methods for designers; and the causal relation retrieval method could serve a technique to bridge the gap between the natural language approach and the modeling approach to biomimetic design.

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.