The increasing implementation of teams in organizations has led to much research attention around team processes and performance. Uncertainty exists, however, in how team processes impact collaborative activities and, ultimately, team performance. Recent research has focused on team cognition as a potential means of explaining this uncertainty. Extending this line of inquiry, my dissertation research focuses on the interplay between teams' cognitive and communicative processes that have been implicitly linked in past team research. Specifically, I examine mental model convergence among team members as a specific type of team cognition. By integrating cognition and communication explicitly, the process of mental model convergence as it unfolds during collaborative activities may be analyzed via the verbal exchange of mental model content. Herein, I compare baseline, intervention, and optimal team communication processes to understand how the communication patterns evoking the underlying mental model convergence process of baseline teams may be changed by team interventions and how the process differs among them. Baseline team data comes from 60 student teams working in a laboratory setting. These data are also used to create a model of team communication processes, which is then implemented to simulate the communication processes of teams receiving interventions. The two types of team intervention conditions investigated include initiating collaborative activities with a specific topic discussion and delaying the start of task activities. The teams with optimal communication processes are obtained using genetic algorithm optimization procedures for combinatorial problems with multiple objectives. Specifically, the genetic algorithm evolves generations of team communication processes, beginning with the baseline data, toward optimal cost and time performance. In addition to examining the mental model convergence process, the performance of intervention teams, analyzed on a neural network generated performance assessment model, is compared to baseline teams receiving no interventions and optimal teams. Results indicate that team interventions do not improve team performance equally. Furthermore, event history analysis indicates a temporal shift in the timing of communication patterns between baseline teams and top intervention teams (i.e., the best performing teams receiving interventions). Moreover, top intervention teams have mental model convergence processes that emulate those of optimal teams.

An analytic approach for the spacing of machines in a windfarm, which can be categorized as either a unidirectional wind or an omnidirectional wind site, was developed. Nonlinear programming problems were formulated to find the spacing for one-line arrays at a given site, in which uncertain turbulence was avoided and the energy generated from the arrays was maximized. The resulting one-line array can then be used as a pattern to apply to the large fixed land windfarm. For unidirectional wind sites, the one-line array was placed parallel to the wind direction. For omnidirectional wind sites, the placement of the array was also discussed. In this study, examples are used to demonstrate the procedure of the mathematical approach. These examples are discussed based on the Lissaman wake model. When compared with equidistant spacing, based on the Lissaman model, some of the resulting spacings by this approach show significant improvement in energy generation. A spacing for a parallel array in an omnidirectional wind site is also discussed. A nonlinear programming problem is formulated to determine the parameters for the optimal array.

The large number of schedules which must be evaluated to develop an optimal solution as opposed to the time required to evaluate a single schedule of the Three Machine Flow-Shop Scheduling Problem when minimizing Makespan, the n/3/F/Cmax problem, causes this problem to be considered unsolvable. The objective of this research is to demonstrate the existence of exploitable structural properties which can be used to construct optimal or near-optimal solutions to the n/3/F/Cmax problem. The interaction of two structural properties identified, the Job Class Decomposition and the Complementary Makespan Paths, has led to theoretical results which reduce the previously smallest solution space known to exist for the n/3/F/Cmax problem; the permutation sequences. These theoretical results indicate that certain subsets of the permutation sequences are always dominated in Makespan Value while other subsets will generally dominate all other permutation sequences in Makespan Value. Each of these subsets are shown to have definable structural properties which exist among the 6 Job Classes defined by the Job Class Decomposition, such as Symmetry 1 and Symmetry 2. Symmetry 1 and Symmetry 2 relate to some strong dependencies which appear to exist among the 6 Job Classes. These strong dependencies suggest that certain sequencing patterns, defined as the Preferred Sequences, generally lead to optimal or near optimal Makespan sequences when implemented in algorithmic form; while other sequencing patterns, defined as the Dominance Categories, lead to sequences that are always dominated in Makespan Value. Eleven Sequencing Rules based upon the properties of the Preferred Sequences and the 6 Job Classes are developed. Four simple constructive algorithms are based upon these sequencing rules. A fifth algorithm is designed to capture all the interrelationships of the properties identified in this thesis. Seven instances of two simple constructive algorithms, one instance each of the two other constructive algorithms, three instances of the fifth algorithm and Palmer's Method are implemented to solve 100 job sets defined in each of six experiments. The results of the six experiments indicate that the fifth algorithm and several instances of the first two simple constructive algorithms, in general, construct a superior Makespan sequence to the one constructed by Palmer's 'Slope index' Method.

Recent advances in the study of fire spread and behavior of building materials under fire have helped designers to set minimum standards for both structural and finishing materials for different types of building occupancies. However, when a fire breaks out in a building, the immediate hazard is to the occupants, yet there are no precisely defined ways of designing adequate means of escape. It is hypothesized that this apparent lack of research in this direction is due in part to the differences in the design of buildings as a result of unique site conditions or the building configuration itself. Coupled with this uniqueness of design is the tendency of humans to panic when an emergency arises leading to unpredictable actions. Given that deterministic models are not capable of handling such unpredictable behavior, designs based purely on the intuition of the designer can lead to very disastrous results in case of an emergency. Two methodologies for the design and evaluation of building facilities and regional emergency evacuation plans have been proposed. A building plan provided by the decision maker is translated into a mathematical format useful for analysis. The analysis is performed and the feasible alternatives given to the designer or decision maker. The methodologies were tested on several examples including evacuation of a medical facility which was used as a case study. Both methodologies for routing and resource allocation efficiently solved the problem, thus aiding the designer in identifying critical parameters. Finally the dissertation proposes future work for the Emergency Evacuation Problem. This work includes incorporating the models that were developed here in a decision support environment. This enhancement would improve the decision making process as it would enable the designer to interactively test various design strategies.

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