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dc.contributor.advisorGerman, Brian J.
dc.contributor.authorHeller, Collin M.
dc.date.accessioned2014-01-13T16:21:43Z
dc.date.available2014-01-13T16:21:43Z
dc.date.created2013-12
dc.date.issued2013-08-29
dc.date.submittedDecember 2013
dc.identifier.urihttp://hdl.handle.net/1853/50272
dc.description.abstractThe research objective of this thesis is to formulate and demonstrate a computational framework for modeling the design decisions of engineers. This framework is intended to be descriptive in nature as opposed to prescriptive or normative; the output of the model represents a plausible result of a designer's decision making process. The framework decomposes the decision into three elements: the problem statement, the designer's beliefs about the alternatives, and the designer's preferences. Multi-attribute utility theory is used to capture designer preferences for multiple objectives under uncertainty. Machine-learning techniques are used to store the designer's knowledge and to make Bayesian inferences regarding the attributes of alternatives. These models are integrated into the framework of a Markov decision process to simulate multiple sequential decisions. The overall framework enables the designer's decision problem to be transformed into an optimization problem statement; the simulated designer selects the alternative with the maximum expected utility. Although utility theory is typically viewed as a normative decision framework, the perspective in this research is that the approach can be used in a descriptive context for modeling rational and non-time critical decisions by engineering designers. This approach is intended to enable the formalisms of utility theory to be used to design human subjects experiments involving engineers in design organizations based on pairwise lotteries and other methods for preference elicitation. The results of these experiments would substantiate the selection of parameters in the model to enable it to be used to diagnose potential problems in engineering design projects. The purpose of the decision-making framework is to enable the development of a design process simulation of an organization involved in the development of a large-scale complex engineered system such as an aircraft or spacecraft. The decision model will allow researchers to determine the broader effects of individual engineering decisions on the aggregate dynamics of the design process and the resulting performance of the designed artifact itself. To illustrate the model's applicability in this context, the framework is demonstrated on three example problems: a one-dimensional decision problem, a multidimensional turbojet design problem, and a variable fidelity analysis problem. Individual utility functions are developed for designers in a requirements-driven design problem and then combined into a multi-attribute utility function. Gaussian process models are used to represent the designer's beliefs about the alternatives, and a custom covariance function is formulated to more accurately represent a designer's uncertainty in beliefs about the design attributes.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectDecision making
dc.subjectGaussian process model
dc.subjectUtility theory
dc.subject.lcshDecision making Mathematical models
dc.subject.lcshDecision making Testing
dc.subject.lcshGaussian processes
dc.subject.lcshEngineering
dc.titleA computational model of engineering decision making
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentAerospace Engineering
thesis.degree.levelMasters
dc.contributor.committeeMemberFeigh, Karen
dc.contributor.committeeMemberParedis, Chris
dc.date.updated2014-01-13T16:21:43Z


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