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Dual response surface optimization based on skill scores

The popular formulations of dual-response optimization are constructed on minimizing a function of bias and system variability. This study provides an opportunity to evaluate the dual response surface (DRS) problem from a different perspective by adapting two new terms such that internal and externa...

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Main Authors:	Kozan, Agah, Zeybek, Melis, Kozan, Elif
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2024
Online Access:	http://journalarticle.ukm.my/23926/1/SE%2015.pdf http://journalarticle.ukm.my/23926/ https://www.ukm.my/jsm/english_journals/vol53num4_2024/contentsVol53num4_2024.html
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id	my-ukm.journal.23926
record_format	eprints
spelling	my-ukm.journal.239262024-08-06T02:07:48Z http://journalarticle.ukm.my/23926/ Dual response surface optimization based on skill scores Kozan, Agah Zeybek, Melis Kozan, Elif The popular formulations of dual-response optimization are constructed on minimizing a function of bias and system variability. This study provides an opportunity to evaluate the dual response surface (DRS) problem from a different perspective by adapting two new terms such that internal and external quality forecasts. The background of the proposed approach focuses on the relationship between internal and external quality forecasts and discusses the DRS problem in regards of skill scores by defining a model quality criterion. Skill is the relative accuracy of the forecast and defines a correspondence between forecast of interest and reference forecasts. The reference forecast does not require any knowledge or modelling; thus, it is an unskilled forecast. In this context, skill score is a measure of this relative improvement and widely used in evaluating the performance of operational and experimental forecasts. An alternative version of mean square error (MSE) which is reconstructed by skill scores and model quality criterion is proposed as an objective function for the DRS problem. Integrating the relationship between internal and external quality forecasts into such a response function can improve the effectiveness and cooperation of the applied technique. The proposed approach has a flexible structure and provides decision makers alternative solutions for different values of the model quality criterion. The proposed procedure is discussed by conducted a simulation study and demonstrated in an engineering process. Penerbit Universiti Kebangsaan Malaysia 2024 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/23926/1/SE%2015.pdf Kozan, Agah and Zeybek, Melis and Kozan, Elif (2024) Dual response surface optimization based on skill scores. Sains Malaysiana, 53 (4). pp. 921-934. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol53num4_2024/contentsVol53num4_2024.html
institution	Universiti Kebangsaan Malaysia
building	Tun Sri Lanang Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Kebangsaan Malaysia
content_source	UKM Journal Article Repository
url_provider	http://journalarticle.ukm.my/
language	English
description	The popular formulations of dual-response optimization are constructed on minimizing a function of bias and system variability. This study provides an opportunity to evaluate the dual response surface (DRS) problem from a different perspective by adapting two new terms such that internal and external quality forecasts. The background of the proposed approach focuses on the relationship between internal and external quality forecasts and discusses the DRS problem in regards of skill scores by defining a model quality criterion. Skill is the relative accuracy of the forecast and defines a correspondence between forecast of interest and reference forecasts. The reference forecast does not require any knowledge or modelling; thus, it is an unskilled forecast. In this context, skill score is a measure of this relative improvement and widely used in evaluating the performance of operational and experimental forecasts. An alternative version of mean square error (MSE) which is reconstructed by skill scores and model quality criterion is proposed as an objective function for the DRS problem. Integrating the relationship between internal and external quality forecasts into such a response function can improve the effectiveness and cooperation of the applied technique. The proposed approach has a flexible structure and provides decision makers alternative solutions for different values of the model quality criterion. The proposed procedure is discussed by conducted a simulation study and demonstrated in an engineering process.
format	Article
author	Kozan, Agah Zeybek, Melis Kozan, Elif
spellingShingle	Kozan, Agah Zeybek, Melis Kozan, Elif Dual response surface optimization based on skill scores
author_facet	Kozan, Agah Zeybek, Melis Kozan, Elif
author_sort	Kozan, Agah
title	Dual response surface optimization based on skill scores
title_short	Dual response surface optimization based on skill scores
title_full	Dual response surface optimization based on skill scores
title_fullStr	Dual response surface optimization based on skill scores
title_full_unstemmed	Dual response surface optimization based on skill scores
title_sort	dual response surface optimization based on skill scores
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2024
url	http://journalarticle.ukm.my/23926/1/SE%2015.pdf http://journalarticle.ukm.my/23926/ https://www.ukm.my/jsm/english_journals/vol53num4_2024/contentsVol53num4_2024.html
_version_	1806689624964202496
score	13.211869

Dual response surface optimization based on skill scores

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