Optimal Accelerated Life-Testing Plans Under Progressive Type II First Failure Censoring Scheme

Designing optimal accelerated life testing (ALT) plans under progressive Type-II first failure censoring involves complex computational challenges, especially when trying to balance efficiency, precision, and practicality. This research introduces a novel optimization framework aimed at determining the best test configuration that minimizes estimation uncertainty within constrained experimental conditions. We developed a tailored meta-heuristic strategy based on an enhanced Variable Neighborhood Search (VNS) algorithm, which efficiently navigates the complex landscape of censoring schemes and stress allocations. Unlike conventional methods that typically focus on simpler censoring or stress-level structures, this approach simultaneously optimizes both the censoring points and sample allocations across various stress levels in an accelerated testing context. The proposed method prioritizes maximizing the determinant of the Fisher Information Matrix under D-optimality, ensuring minimal variance in parameter estimation. Extensive simulation studies demonstrate the robustness and computational efficiency of the algorithm, showing significant improvements over traditional techniques, particularly in large-scale ALT scenarios. Furthermore, the framework takes into account real-world constraints such as limited test durations and maximum allowable censoring, enhancing its practical applicability. This work advances the field by extending optimal test plan methodologies to more realistic censoring schemes, providing a powerful decision-support tool for reliability engineers and researchers in accelerated life testing environments.