Optimization of generalized desirability functions under model uncertainty. (2nd December 2017)

• Record Type: Journal Article
• Authors: Akteke-Öztürk, Başak; Weber, Gerhard-Wilhelm; Köksal, Gülser
• Is Part Of: Optimization. Volume 66:Number 12(2017)
• Page Start: 2157
• Page End: 2169
• Publication Date: 2017-12-02
• Subjects: Multi-response surface optimization -- desirability function -- model uncertainty -- generalized semi-infinite optimization -- disjunctive optimization; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2017.1371167
• Languages: English
• ISSNs: 0233-1934

Abstract

Abstract: Desirability functions are increasingly used in multi-criteria decision-making which we support by modern optimization. It is necessary to formulate desirability functions to obtain a generalized version with a piecewise max type-structure for optimizing them in different areas of mathematics, operational research, management science and engineering by nonsmooth optimization approaches. This optimization problem needs to be robustified as regression models employed by the desirability functions are typically built under lack of knowledge about the underlying model. In this paper, we contribute to the theory of desirability functions by our robustification approach. We present how generalized semi-infinite programming and disjunctive optimization can be used for this purpose. We show our findings on a numerical example. The robustification of the optimization problem eventually aims at variance reduction in the optimal solutions.