Optimization of generalized desirability functions under model uncertainty

Akteke-Ozturk B., Weber G., Köksal G.

OPTIMIZATION, cilt.66, ss.2157-2169, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1080/02331934.2017.1371167
  • Dergi Adı: OPTIMIZATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2157-2169
  • Anahtar Kelimeler: Multi-response surface optimization, desirability function, model uncertainty, generalized semi-infinite optimization, disjunctive optimization, ROBUST OPTIMIZATION, REGRESSION, ROBUSTIFICATION, FINANCE, RCMARS, NETWORKS, DYNAMICS, SPLINE
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.