Compromise design settings for multiple responses

The authors review and compare the techniques doc-umented in the statistical literature for finding a vec-tor x of design variable settings, which produces the optimal compromise solution among a group of pri-oritized response variables. The best compromise so-lution is typically gained by optimizing an objective function, which incorporates the prioritized demands of the multiple responses. Since most multi-response objective functions are constructed from combining the functions used to optimize univariate responses, a review of the prominent univariate approaches is presented first. A multivariate approach from the en-gineering literature called the compromise Decision Support Problem is also reviewed. Finally a table comparing the relative merits of the different multi-variate approaches summarizes the article in a concise and user-friendly fashion.