Koksoy, O2019-08-012019-08-0120060096-3003https://dx.doi.org/10.1016/j.amc.2005.09.016https://hdl.handle.net/11480/5525Most of the published literature on robust design methodology is basically concerned with optimization of a single response or quality characteristic which is often most critical to consumers. However, manufactured products are typically characterized by numerous quality characteristics. In this paper we present a general framework for the multivariate problem when data are collected from a combined array. Within the framework, a mean square error (MSE) criterion is utilized and a non-linear multiobjective programming problem based on the individual MSE functions of each response is proposed for quality improvement. We adapted a suitable non-linear optimization algorithm to solve the proposed formulation. The optimization method used in this paper generates a string of solutions, called Pareto optimal solutions, rather than a "one shot" optimum solution to make selections and evaluate the trade-offs. The paper also presents an example and comparative results in order to demonstrate the potentials of the proposed approach. (c) 2005 Elsevier Inc. All rights reserved.eninfo:eu-repo/semantics/closedAccessrobust designmultiresponse optimizationmean square errorcombined arraymultiobjective non-linear programmingArticle17521716172910.1016/j.amc.2005.09.0162-s2.0-33645869836Q1WOS:000237568000058Q2