Implementation of the sequential optimal design strategy in Type-II progressive censoring with the GLM-based mechanism

Single-objective optimal designs might be criticized for not covering all aspects of the experiment when the experiment possesses multiple goals. In such a case, multi-objective optimal design is of interest. This paper adopts a sequential approach to obtain a multi-objective optimal design for Type-II progressive censoring with a dependent GLM-based random removal mechanism. Several simulation studies are conducted to evaluate and compare the performance of the proposed approach. A sensitivity analysis has been performed to investigate the effect of misspecification of design input parameters. Also, the sequential optimal design solution is used to construct the bounds in the \(\epsilon\) $ϵ$ -constraint optimal design. Finally, the usefulness of the proposed strategy is demonstrated through two real-life data analyses.