Sidney Addelman

Bio: Sidney Addelman is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 5099 citations.

Response Surface Methodology: Process and Product Optimization Using Designed Experiments

Abstract: From the Publisher: Using a practical approach, it discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniques, focusing on response surface methodology, mixture experiments and robust design techniques. Features numerous authentic application examples and problems. Illustrates how computers can be a useful aid in problem solving. Includes a disk containing computer programs for a response surface methodology simulation exercise and concerning mixtures.

Efficient Global Optimization of Expensive Black-Box Functions

Abstract: In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.

Empirical Model-Building and Response Surfaces

Abstract: Introduction to Response Surface Methodology. The Use of Graduating Functions. Least Squares for Response Surface Work. Factorial Designs at Two Levels. Blocking and Fractionating 2 k Factorial Designs. The Use of Steepest Ascent to Achieve System Improvement. Fitting Second--Order Models. Adequacy of Estimation and the Use of Transformation. Exploration of Maxima and Ridge Systems with Second--Order Response Surfaces. Occurrence and Elucidation of Ridge Systems, I. Occurrence and Elucidation of Ridge Systems, II. Links Between Emprirical and Theoretical Models. Design Aspects of Variance, Bias, and Lack of Fit. Variance----Optimal Designs. Practical Choice of a Response Surface Design. Subject Index. Index.