In many industrial experiments there are restrictions on the resource (or cost) required for performing the runs in a response surface design. This will require practitioners to choose some subset of the candidate set of experimental runs. The appropriate selection of design points under resource constraints is an important aspect of multi-factor experimentation. A well-planned experiment should consist of factor-level combinations selected such that the resulting design will have desirable statistical properties but the resource constraints should not be violated or the experimental cost should be minimized. The resulting designs are referred to as cost-efficient designs. We use a genetic algorithm for constructing cost-constrained G-efflcient second-order response surface designs over cuboidal regions when an experimental cost at a certain factor level is high and a resource constraint exists. Consideration of practical resource (or cost) restrictions and different cost structures will provide valuable information for planning effective and economical experiments when optimizing statistical design properties.
- Cost restriction
- Cuboidal design region
- Genetic algorithms
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Management Science and Operations Research