Early Risk Quantification Strategy for Design Space Reduction Decisions in Set-Based Design
J.B. Van Houten (University of Michigan)
Austin Kana (TU Delft - Ship Design, Production and Operations)
D.J. Singer (University of Michigan)
M.D. Collette (University of Michigan)
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Abstract
Perceptions of feasibility in design spaces are susceptible to change if new and conflicting information becomes available later. Design space reduction decisions made in set-based design can amplify vulnerability to new information if remaining design spaces and present perceptions are unable to adapt. This paper considers different ways new information can alter perceptions of feasibility for complex design problems and introduces an early, probabilistic strategy for quantifying the risk of eliminating potential design solutions based on the vulnerability of remaining design spaces to new information. Emergent designs of a set-based design process gauging this risk are evaluated against one neglecting it for an analogous design problem. Early results indicate that the probabilistic model is able to effectively delay design decisions and prevent lock-in while design space understanding is still growing.
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