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dc.contributor.authorKnerr, Nathan
dc.date.accessioned2020-08-10T20:24:24Z
dc.date.available2020-08-10T20:24:24Z
dc.date.issued2020-05
dc.identifier.otherKnerr_cornellgrad_0058F_11914
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:11914
dc.identifier.urihttps://hdl.handle.net/1813/70440
dc.description201 pages
dc.description.abstractIn the early phase design of engineering systems, it has become increasingly popular to use a system model and an optimizer to generate a Pareto Frontier of designs that satisfy certain objectives or criteria (e.g. cost, performance, risk). A decision maker then considers this design space and considers which design appears to best satisfy a stakeholder’s requirements. While the generation of alternatives is relatively straightforward, the stakeholder must still make sense of the potentially thousands of alternatives. Additionally, stakeholders often want to consider the possibility of changing priorities, market segmentation via design families and decision sensitivity for further investigation of system priorities. In practice, stakeholders also have many criteria (5 or more), which can be difficult to visualize or represent simultaneously. As such, we aim to create new tools and techniques to extract or aid in finding this information from large multi-criteria decision problems. We approach this with three techniques. The first, Cityplot, uses a virtual reality representation of the design space by use of dimension reduction to represent both the engineering decisions that create a design and the criteria that a stakeholder would care about. A human study is performed to show the validity of the approach by having human subjects perform basic tests and evaluations of real and synthetic design problems. We find the Cityplot allows the intuitive visualization of decisions and a large number of objectives. The second, MOMS-CFCs, provides an interpretation of a single linkage tree and matching procedure to find rules of thumb regarding system dependencies and decision sensitivities. While promising, a few key issues prevent the approach from being applicable in most practical applications. The third, MAPSA, models the shape of the Pareto frontier with a mean plane which parameterizes the space and a model of the residuals which characterizes the overall shape of the frontier. This enables characterization of the tradeoffs and summarization of key features of the frontier. We show some simple mathematical results and create a rough model of how such an approach can be useful for decision making.
dc.language.isoen
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDesign Space Exploration
dc.subjectDesign Statistics
dc.subjectEngineering Design
dc.subjectMulti-Criteria Decision Making
dc.subjectMulti-Objective Design Analytics
dc.subjectVisualization
dc.titleLOST IN DESIGN SPACE: INTERPRETING RELATIONS/STRUCTURE BETWEEN DECISIONS AND OBJECTIVES IN ENGINEERING DESIGN
dc.typedissertation or thesis
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Mechanical Engineering
dc.contributor.chairHoffman, Guy
dc.contributor.committeeMemberSelva Valero, Daniel
dc.contributor.committeeMemberFrazier, Peter
dc.contributor.committeeMemberSridharan, Karthik
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/pkzh-1749


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