Constructing model robust mixture designs via weighted G-optimality criterion

Wanida Limmun, Boonorm Chomtee, John Borkowski
2019 International Journal of Industrial Engineering Computations  
We propose and develop a new G-optimality criterion using the concept of weighted optimality criteria and certain additional generalizations. The goal of the weighted G-optimality is to minimize a weighted average of the maximum scaled prediction variance in the design region over a set of reduced models. A genetic algorithm (GA) is used for generating the weighted Goptimal exact designs in an experimental region for mixtures. The performance of the proposed GA designs is evaluated and compared
more » ... luated and compared to the performance of the designs produced by our genetic algorithm and the PROC OPTEX exchange algorithm of SAS/QC. The evaluation demonstrates the advantages of GA designs over the designs generated using exchange algorithm, showing that the proposed GA designs have better model-robust properties and perform better than the designs generated by the PROC OPTEX exchange algorithm.
doi:10.5267/j.ijiec.2019.4.004 fatcat:tq7w7jlzlvfh5biqetks4sjs4u