On optimum life-testing plans under Type-II progressive censoring scheme using variable neighborhood search algorithm

In determination of optimum Type-II progressive censoring scheme, the experimenter needs to carry out an exhaustive search within the set of all admissible censoring schemes. The existing recommendations are only applicable for small sample sizes. The implementation of exhaustive search techniques for large sample sizes is not feasible in practice. In this article, a meta-heuristic algorithm based on variable neighborhood search approach is proposed for large sample sizes. It is found that the algorithm gives exactly the same solution for small sample sizes as the solution obtained in an exhaustive search; however, for large sample sizes, it gives near-optimum solution. We have proposed a cost function-based optimum criterion, which is scale invariant for location-scale and log-location-scale families of distribution. A sensitivity analysis is also considered to study the effect of misspecification of parameter values or cost coefficients on the optimum solution.