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In determination of optimum Type-II progressive censoring scheme, the
experimenter needs to carry out an exhaustive search within the set of all admissi-
ble censoring schemes. The existing recommendations are only applicable for small
sample sizes. The implementation of exhaustive search techniques for large sam-
ple 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 distribu-
tion. A sensitivity analysis is also considered to study the effect of misspecification of
parameter values or cost coefficients on the optimum solution.