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Probabilistic Analytical Target Cascading: A Moment Matching Formulation for Multilevel Optimization Under Uncertainty

[+] Author Affiliations
Huibin Liu, Wei Chen

Northwestern University, Evanston, IL

Michael Kokkolaras, Panos Y. Papalambros

University of Michigan, Ann Arbor, MI

Harrison M. Kim

University of Illinois at Urbana-Champaign, Urbana, IL

Paper No. DETC2005-84928, pp. 1173-1182; 10 pages
  • ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 31st Design Automation Conference, Parts A and B
  • Long Beach, California, USA, September 24–28, 2005
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4739-X | eISBN: 0-7918-3766-1
  • Copyright © 2005 by ASME


Analytical target cascading (ATC) is a methodology for hierarchical multilevel system design optimization. In previous work, the deterministic ATC formulation was extended to account for uncertainties using a probabilistic approach. Random quantities were represented by their expected values, which were required to match among subproblems to ensure design consistency. In this work, the probabilistic formulation is augmented to allow introduction and matching of additional probabilistic characteristics. Applying robust design principles, a particular probabilistic analytic target cascading (PATC) formulation is proposed by matching the first two moments of random quantities. Several implementation issues are addressed, including representation of probabilistic design targets, matching interrelated responses and linking variables under uncertainty, and coordination strategies for multilevel optimization. Analytical and simulation-based optimal design examples are used to illustrate the new PATC formulation. Design consistency is achieved by matching the first two moments of interrelated responses and linking variables. The effectiveness of the approach is demonstrated by comparing PATC results to those obtained using a probabilistic all-in-one (PAIO) formulation.

Copyright © 2005 by ASME



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