Effects of Robust Convex Optimization on Early-Stage Design Space Exploratory Behavior
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md_142_12_121704.pdf
Description
Published version
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624.73 KB
Format
Adobe PDF
Checksum (MD5)
16678f5035471dc5db737cf6c7c7d123
Author(s)
Pillai, Priya P
Burnell, Edward
Wang, Xiqing
Yang, Maria C
Date Issued
2020
Journal
Journal of Mechanical Design
Publisher
ASME International
Citation
Pillai, Priya P, Burnell, Edward, Wang, Xiqing and Yang, Maria C. 2020. "Effects of Robust Convex Optimization on Early-Stage Design Space Exploratory Behavior." Journal of Mechanical Design, 142 (12).
Version
Final published version
Abstract
Abstract
Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set-based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed the present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant’s understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4048580
