Computation of Optimal Type-II Progressing Censoring Scheme Using Genetic Algorithm Approach

TL;DR

This paper introduces a genetic algorithm-based method to efficiently compute optimal type-II progressing censoring schemes in large-scale life-testing experiments, overcoming the limitations of exhaustive search.

Contribution

It presents a novel meta-heuristic approach that finds optimal or near-optimal censoring schemes for large samples, with a scale-invariant criterion and sensitivity analysis.

Findings

Genetic algorithm effectively finds optimal schemes for large samples.

The method performs well compared to exhaustive search for small samples.

Sensitivity analysis shows robustness to parameter inaccuracies.

Abstract

The experimenter must perform a legitimate search in the entire set of feasible censoring schemes to identify the optimal type II progressive censoring scheme, when applied to a life-testing experiment. Current recommendations are limited to small sample sizes. Exhaustive search strategies are not practically feasible for large sample sizes. This paper proposes a meta-heuristic algorithm based on the genetic algorithm for large sample sizes. The algorithm is found to provide optimal or near-optimal solutions for small sample sizes and large sample sizes. Our suggested optimal criterion is based on the cost function and is scale-invariant for both location-scale and log-location-scale distribution families. To investigate how inaccurate parameter values or cost coefficients may affect the optimal solution, a sensitivity analysis is also taken into account.