The sequential optimal design strategy in Type-II progressive censoring with dependent random removal‎

Single-objective optimal Type-II progressive censoring designs have widely been studied in a life-testing experiment‎. ‎However‎, ‎they might be criticized for not covering all aspects of the experiment when the experiment possesses multiple goals‎. ‎In such a case‎, ‎multi-objective design is of interest‎. ‎In this paper‎, ‎a sequential optimal design as a multi-objective optimal design has been adopted for a Type-II progressive censoring design with dependent random removal model‎. ‎The proposed approach involves three objective criteria‎, ‎including the cost of censoring‎, ‎duration of experiment‎, ‎and variance measure‎. ‎Several simulation studies are conducted to evaluate and compare the performance of the proposed approach‎.