The use of nested desirability functions and quality indices for multi-response robust parameter design problems

ABSTRACT

A multi-response robust parameter design (RPD) problem-solving technique based on desirability functions is presented in which the means and variances of all responses are placed on a level playing field. The proposal allows a decision maker to integrate their preferences for the individual means and variances. It is shown that the ensuing operating point is a system setting that produces a mutually robust set of responses. In addition, this article offers an approach to assess several RPD strategies via quality indices. The measures presented here allow for a more knowledgeable and comprehensive evaluation of the competing RPD strategies.

KEYWORDS:

• desirability functions
• multiple attribute decision-making
• multiple response optimization
• multiple response problems
• parameter design
• robust design
• robust parameter design

Additional information

Notes on contributors

Joseph P. Bellucci

About the authors

Joseph P. Bellucci received his B.S. in Mathematics and Physics from Cumberland University in Lebanon, Tennessee in 2001 and M.S. and Ph.D. in Operations Research from the Air Force Institute of Technology in 2007 and 2016, respectively. He currently serves in the United States Air Force at Randolph AFB, Texas where he is the Chief Scientist at Air Education and Training Command, Studies and Analysis Squadron. His research interests are in the areas of multivariate statistics, robust design, and optimization.

Kenneth W. Bauer

Kenneth W. Bauer is a Professor of Operations Research at the Air Force Institute of Technology where he teaches classes in applied statistics and pattern recognition. His research interests lie in the areas of automatic target recognition and multivariate statistics. He received his B.S. from Miami University at Ohio in 1976, MEA from University of Utah in 1980, M.S. from Air Force Institute of Technology in 1981, and Ph.D. from Purdue University in 1987.