Optimization of generalized desirability functions under model uncertainty

Akteke-Ozturk, Başak
Weber, Gerhard-Wilhelm
Köksal, Gülser
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.


Öztürk, Başak; Köksal, Gülser; Weber, Gerhard Wilhelm (2010-02-04)
Desirability functions of Derringer and Suich, one of the widely used approaches in multiresponse optimization, have nondifferentiable points in their formulations as a drawback. To solve the optimization problem of the overall desirability function, one way is to modify the individual desirability functions by approximation approaches and then to use the gradient based methods. Another way is to use the optimization techniques that do not employ the derivative information. In this study, we propose a new a...
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Citation Formats
B. Akteke-Ozturk, G.-W. Weber, and G. Köksal, “Optimization of generalized desirability functions under model uncertainty,” OPTIMIZATION, pp. 2157–2169, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35922.