Using Pareto Trace to Determine System Passive Value Robustness
Name
Ross-2009-Using Pareto Trace to Determine System Passive Value Robustness.pdf
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Author(s)
Hastings, Daniel E.
Ross, Adam Michael
Rhodes, Donna H
Date Issued
March 2009
Journal
IEEE Systems Conference
Publisher
Institute of Electrical and Electronics Engineers
Citation
Ross, A.M., D.H. Rhodes, and D.E. Hastings. “Using Pareto Trace to determine system passive value robustness.” Systems Conference, 2009 3rd Annual IEEE. 2009. 285-290. © 2009, IEEE
Version
Final published version
Abstract
An important role of system designers is to effectively
explore the tradespace of alternatives when making design
decisions during concept phase. As systems become more
complex, formal methods to enable good design decisions are
essential; this can be empowered through a tradespace
exploration paradigm. This paper demonstrates the use of the
Pareto Trace and associated metrics to identify system
alternatives across tradespaces with high degrees of passive value
robustness—alternatives that continue to deliver value to
stakeholders in spite of changes in needs (attributes) or context.
A value-driven tradespace approach is used to represent the
baseline performance versus cost of a large number of system
alternatives. The classical notion of Pareto Set is extended to
identify alternatives and their characteristics that lead to their
inclusion in Pareto Sets across changing contexts. Using a lowearth
orbiting satellite case example, five types of context changes
are used to demonstrate this method: 1) addition or subtraction
of attributes; 2) change in the priorities of attributes; 3) change in
single attribute utility function shapes; 4) change in multiattribute
utility aggregation function; and 5) addition of new
decision maker. This approach demonstrates the ability for
system designers to pose questions about assessment of
alternatives during early conceptual design. Suggestions for
application of Pareto Trace beyond the case example are
discussed and presented, including application of a “fuzziness”
factor and statistical measures. In particular, distinctions from
traditional sensitivity analysis are made, as well as linkages to
dynamic analysis for discovery of generalized value robust
alternatives.
MIT Department
Massachusetts Institute of Technology. Center for Technology, Policy, and Industrial Development
Massachusetts Institute of Technology. Engineering Systems Division
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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.
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DOI of Published Version
https://doi.org/10.1109/SYSTEMS.2009.4815813
