Enhanced probabilistic analytical target cascading with application to multi-scale design

F. Xiong, X. Yin, W. Chen*, S. Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Probabilistic analytical target cascading (PATC) is an approach for multi-level multi-disciplinary design optimization under uncertainty. In the original PATC approach, only the mean and variance of each interrelated response and linking variable are matched in a multi-level hierarchy. The ignorance of response correlation introduces difficulties in finding optimal solutions especially when the covariance of interrelated responses has a significant impact. In this article, an enhanced PATC (EPATC) approach is proposed. In addition to matching the first two statistical moments, the covariance between the interrelated responses is also considered by applying a modified updating strategy for estimating the statistical performance of an upper-level subsystem. A mathematical example and a multi-scale design problem are used to demonstrate the effectiveness and efficiency of the proposed EPATC approach. This study shows that the EPATC approach outperforms the original PATC by providing more accurate optimal solutions.

Original languageEnglish (US)
Pages (from-to)581-592
Number of pages12
JournalEngineering Optimization
Issue number6
StatePublished - Jun 1 2010


  • Correlated response
  • Multi-level optimization
  • Multi-scale design
  • Probabilistic analytical target cascading
  • Uncertainty

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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