An illustrative case study on application of learning based ordinal optimization approach to complex deterministic problem

Abstract

In this paper we consider complex deterministic problems, where there are two models that can be used to predict the performance for a given design. One of the models can give a precise estimation, but is complex and time consuming. The other model is simple and fast, but can only give a very crude estimation. We have proposed a learning-based ordinal optimization approach to tackle this problem. In this approach, we first run a simple model for all the designs and a complex model for a few designs, and then, through regression analysis, we estimate the noise trend, and this noise trend together with the crude estimates from the simple model will be used to screen the designs. The proposed approach is applied to solve an integrally bladed rotor (IBR) manufacturing problem where the production sequence and the production parameters need to be determined in order to minimize the overall manufacturing cost while satisfying the manufacturing constraints. The results indicate that, by using a very crude and simple model, we are able to identify good designs with a high degree of confidence. © 2005 Elsevier B.V. All rights reserved.