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Most research in design of experiments focuses on appropriate designs for a system with just one type of response, rather than multiple responses. In a decision‐making process, relying on only one objective can lead to oversimplified, suboptimal choices that ignore important considerations. Consequently, the problem of constructing a design for an experiment when multiple types of responses are of interest often does not have a single definitive answer, particularly when the response variables have different distributions. Each of these response distributions imposes different requirements on the experimental design. Computer‐generated optimal designs are popular design choices for less standard scenarios where classical designs are not ideal. This work presents a new approach to experimental designs for dual‐response systems. The normal and binomial distributions are considered as potential responses. Using the D‐criterion for the linear model and the Bayesian D‐criterion for the logistic regression model, a weighted criterion is implemented in a coordinate‐exchange algorithm. Designs are evaluated and compared across different weights. The sensitivity of the designs to the priors supplied for the Bayesian D‐criterion is also explored. [ABSTRACT FROM AUTHOR]

Classical D-optimal design is used to create experimental designs for situations in which an underlying system model is known or assumed known. The D-optimal strategy can also be used to add additional experimental runs to an existing design. This paper demonstrates a study of variable choices related to sequential D-optimal design and how those choices influence the D-efficiency of the resulting complete design. The variables studied are total sample size, initial experimental design size, step size, whether or not to include center points in the initial design, and complexity of initial model assumption. The results indicate that increasing total sample size improves the D-efficiency of the design, less effort should be placed in the initial design, especially when the true underlying system model isn't known, and it is better to start off with assuming a simpler model form, rather than a complex model, assuming that the experimenter can reach the true model form during the sequential experiments. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]