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.