Optimal designs are computer-generated experimental designs that provide an experimenter with an ‘optimal’ set of experimental trials. Historically, optimal experimental design has been limited to optimization with regards to a single criterion for a single response variable. Recent research by Burke et al. (2017) made it possible to create a dual response optimal designs for cases involving experiments with one continuous response and one binary response. The algorithm in Burke et al. (2017) provides a series of weighted optimal designs across a range of weights between the continuous and binary response cases. This thesis extends the work by Burke et al. (2017) in three ways. First, a new optimality criterion is developed in order to provide more stable algorithm results. Second, a method for selecting the weighted design that provides the best results for the continuous and binary cases is developed. Finally, a sensitivity analysis on the prior information required to generate the optimal designs in performed.

Publication Date


Document Type


Student Type


Degree Name

Industrial and Systems Engineering (MS)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)


Rachel Silvestrini

Advisor/Committee Member

Katie McConky


RIT – Main Campus