Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2019

Efficient algorithms for constructing D- and I-optimal exact designs for linear and non-linear models in mixture experiments

  • Identification data

    Identifier: RP:3413
    Handle: https://hdl.handle.net/20.500.11797/RP3413
    Authors:
    Torsney, BernardGarcía-Camacha, IreneMartín, Raúl
    Abstract:
    The problem of finding optimal exact designs is more challenging than that of approximate optimal designs. In the present paper, we develop two efficient algorithms to numerically construct exact designs for mixture experiments. The first is a novel approach to the well-known multiplicative algorithm based on sets of permutation points, while the second uses genetic algorithms. Using (i) linear and non-linear models, (ii) D/- and I-optimality criteria, and (iii) constraints on the ingredients, both approaches are explored through several practical problems arising in the chemical, pharmaceutical and oil industry.

  • Others:

    URV's Author/s: Torsney, Bernard García-Camacha, Irene Martín, Raúl
    Keywords: Optimal experimental design, D-optimality, I-optimality, mixture experiments, multiplicative algorithm, genetic algorithm, exact designs
    Abstract: The problem of finding optimal exact designs is more challenging than that of approximate optimal designs. In the present paper, we develop two efficient algorithms to numerically construct exact designs for mixture experiments. The first is a novel approach to the well-known multiplicative algorithm based on sets of permutation points, while the second uses genetic algorithms. Using (i) linear and non-linear models, (ii) D/- and I-optimality criteria, and (iii) constraints on the ingredients, both approaches are explored through several practical problems arising in the chemical, pharmaceutical and oil industry.
    Journal publication year: 2019
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Optimal experimental design, D-optimality, I-optimality, mixture experiments, multiplicative algorithm, genetic algorithm, exact designs

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