Universal Alignment Probabilities and Subset Selection for Ordinal Optimization

  • T. W. Edward Lau
  • Y. C. Ho

DOI: 10.1023/A:1022614327007

Cite this article as:
Lau, T.W.E. & Ho, Y.C. Journal of Optimization Theory and Applications (1997) 93: 455. doi:10.1023/A:1022614327007


We examine in this paper the subset selection procedure in the context of ordinal optimization introduced in Ref. 1. Major concepts including goal softening, selection subset, alignment probability, and ordered performance curve are formally introduced. A two-parameter model is devised to calculate alignment probabilities for a wide range of cases using two different selection rules: blind pick and horse race. Our major result includes the suggestion of quantifiable subset selection sizes which are universally applicable to many simulation and modeling problems, as demonstrated by the examples in this paper.

Subset selection stochastic optimization alignment probability ordered performance curve simulation modeling 

Copyright information

© Plenum Publishing Corporation 1997

Authors and Affiliations

  • T. W. Edward Lau
    • 1
  • Y. C. Ho
    • 2
  1. 1.Division of Engineering and Applied SciencesHarvard UniversityCambridge
  2. 2.Division of Engineering and Applied SciencesHarvard UniversityCambridge

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