Multicriteria steepest ascent in a design space consisting of both mixture and process variables

C. A. A. Duineveld; P. M. J. Coenegracht

doi:10.1016/0169-7439(95)00033-X

Multicriteria steepest ascent in a design space consisting of both mixture and process variables

C. A. A. Duineveld^*
, P. M. J. Coenegracht

^*Corresponding author voor dit werk

Onderzoeksoutput › Academic › peer review

3 Citaten (Scopus)

Samenvatting

Steepest ascent is shown to be a feasible method for problems where two or more responses are to be optimized. With the aid of Pareto optimality the (one response) standard method is adapted for the use of more responses. A special kind of steepest ascent problem involves the presence of both mixture and process variables. This problem is solved by the use of projection designs. The combined method is demonstrated with a simulated problem.

Originele taal-2	English
Pagina's (van-tot)	23-36
Aantal pagina's	14
Tijdschrift	Chemometrics and Intelligent Laboratory Systems
Volume	30
Nummer van het tijdschrift	1
DOI's	https://doi.org/10.1016/0169-7439(95)00033-X
Status	Published - nov.-1995

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Multicriteria steepest ascent in a design space consisting of both
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abstract = "Steepest ascent is shown to be a feasible method for problems where two or more responses are to be optimized. With the aid of Pareto optimality the (one response) standard method is adapted for the use of more responses. A special kind of steepest ascent problem involves the presence of both mixture and process variables. This problem is solved by the use of projection designs. The combined method is demonstrated with a simulated problem.",

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AU - Duineveld, C. A. A.

AU - Coenegracht, P. M. J.

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KW - experimental design

KW - linear regression

KW - response surface

KW - multicriteria

KW - DECISION-MAKING

KW - COMBINING PROCESS

KW - OPTIMIZATION

U2 - 10.1016/0169-7439(95)00033-X

DO - 10.1016/0169-7439(95)00033-X

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JF - Chemometrics and Intelligent Laboratory Systems

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ER -

Multicriteria steepest ascent in a design space consisting of both mixture and process variables

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