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Adaptive Learning for Successful Design

  • Dan Braha
  • Oded Maimon
Chapter
  • 468 Downloads
Part of the Applied Optimization book series (APOP, volume 17)

Abstract

In this chapter we want to evaluate which of many parameters (each is set at various possible levels) composing a design solution have the greatest likelihood of satisfying a given set of functional requirements. The design’s functional requirements are represented by a set of prespecified limits that determine where the output responses should fall. Adopting the probabilistic paradigm presented in Chapter 7 and the methodology provided in Chapter 8 for quantifying how well a proposed design satisfies the governing requirements (in probabilistic terms), we present a method for adaptive learning of successful designs that is based on the use of statistical experimental design and a stochastic search algorithm. In Chapter 19, we present a real industrial problem of designing a flexible manufacturing system that is solved based on the proposed algorithm.

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References

  1. 1.
    Ross, P. J., Taguchi Techniques for Quality Engineering, McGraw-Hill, 1988.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Dan Braha
    • 1
  • Oded Maimon
    • 2
  1. 1.Department of Industrial EngineeringBen Gurion UniversityBeer ShevaIsrael
  2. 2.Department of Industrial EngineeringTel-Aviv UniversityTel-AvivIsrael

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