Optimization Under Uncertainty Based on Multiparametric Kriging Metamodels

Ahmed Shokry () and Antonio Espuña ()
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Ahmed Shokry: Universitat Politecnica de Catalunya
Antonio Espuña: Universitat Politecnica de Catalunya

A chapter in Operations Research Proceedings 2015, 2017, pp 575-581 from Springer

Abstract: Abstract Different reasons can hinder the application of multiparametric programming formulations to solve optimization problems under uncertainty, as the high nonlinearity of the optimization model, and/or its complicated structure. This work presents a complementary method that can assist in such situations. The proposed tool uses kriging metamodels to provide global multiparametric metamodels that approximate the optimal solutions as functions of the problem uncertain parameters. The method has been tested with two benchmark problems of different characteristics, and applied to a case study. The results show the high accuracy of the methodology to predict the multiparametric behavior of the optimal solution, high robustness to deal with different problem types using small number of data, and significant reduction in the solution procedure complexity in comparison with classical multiparametric programming approaches.

Keywords: Sampling Plan; Ordinary Kriging; Steam Flowrate; Outlet Steam; Process Model Parameter (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-42902-1_78

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DOI: 10.1007/978-3-319-42902-1_78

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