Reliability Acceptance Sampling Plans Using Compound Optimal Design Under Hybrid Censoring

In this study, we develop a reliability acceptance sampling plan (RASP) for evaluating product acceptability based on lifetime data. It ensures that only products meeting the required reliability standards are accepted by focusing on relevant lifetime characteristics. A key focus of this work is determining the optimal sample size for the RASP, which we achieve using a compound optimal design (COD) strategy. The COD approach is efficient because it optimizes two objective functions simultaneously. It ensures that the sampling plan is statistically reliable and cost-effective. We have used the extreme value distribution for demonstration purposes, but this can be extended to any location–scale family. In addition, we present a graphical solution approach that simplifies the computation and interpretation of the COD, enabling practitioners to visualize and select optimal designs more efficiently. Additionally, we perform a sensitivity analysis to explore the impact of potential model parameter misspecifications on the sampling plan.