Computing Optimal Multi-Level Stress Testing Plans Using A Combined Variable Neighborhood Search Algorithm Under Progressive Type-II Censoring Scheme

In multi-level stress life tests under Type-II progressive censoring, determining optimal allocation poses significant computational challenges due to the vast solution space. Efficient methods are essential for exploring the admissible censoring schemes effectively. This thesis introduces a novel meta-heuristic algorithm, the Combined Variable Neighborhood Search (CVNS), which computes optimal schemes at different stress levels simultaneously. Unlike methods focusing on marginal stress levels or one-step progressive censoring, this approach leverages a unified framework to ensure enhanced computational efficiency and solution quality. By integrating the components of the design parameters into a cohesive optimization process, the algorithm effectively reduces computational time while providing near-optimal solutions. This CVNS algorithm demonstrates consistency with exhaustive search results for small-scale scenarios. Extensive numerical studies reveal its applicability across diverse stress levels and censoring proportions, offering robust solutions for maximizing the determinant of the Fisher Information Matrix under D-optimality and other relevant criteria. Additionally, the algorithm accommodates constraints on the degree of censoring and sample allocation, making it versatile for practical experimental designs. The proposed method addresses gaps in existing approaches by incorporating general Type-II progressive censoring for optimal multi-level stress tests and expands upon earlier works that were limited to simpler models or smaller scales. This advancement provides a valuable tool for experimenters seeking to optimize life-testing plans under complex conditions.