The simulation model is a proven tool in solving nonlinear and stochastic problems and allows examination of the likely behavior of a proposed manufacturing system under selected conditions. However, it does not provide a method for optimization. A practical problem often embodies many characteristics of a multiresponse optimization problem. The present paper proposes to solve the multiresponse...

The normal distribution approach is often used in regression analysis at the Response Surface Methodology (RSM) modeling stage. Several studies have shown that has drawbacks compared to more robust t-distribution approach. found control size much successfully small samples existing methods presence of moderately heavy tails. In many RSM applications, there than one response (multiresponse), whi...

The well-known quality improvement methodology, robust design, is a powerful and cost-effective technique for building quality into the design of products and processes. Although several approaches to robust design have been proposed in the literature, little attention has been given to the development of a flexible robust design model. Specifically, flexibility is needed in order to consider m...

This investigation proposes a grey-based Taguchi method to solve the multi-response problems. The grey-based Taguchi method is based on the Taguchi’s design of experimental method, and adopts grey relational analysis (GRA) to transfer multiresponse problems into single-response problems. In this investigation, an attempt has been made to optimize the drilling process parameters considering weig...

The paper is concerned with a problem of finding an optimum experimental design for discriminating between two rival multiresponse models. The criterion of optimality we use is based on the sum of squares of deviations between the models, and picks up the design points for which the divergence is maximum. An important part of our criterion is an additional vector of experimental conditions, whi...