Computation of optimum Type-II progressively hybrid censoring schemes using variable neighborhood search algorithm

Type-II progressively hybrid censoring scheme is a mixture of Type-II progressive censoring and Type-I censoring schemes. In this article, we first derive the expression of Fisher information matrix based on Type-II progressively hybrid censored data for multi-parameter distribution. We then propose a cost minimization-based optimality criterion to determine optimum Type-II progressively hybrid censoring schemes. Determination of optimum schemes through exhaustive search within the set of all admissible censoring schemes for large sample sizes is not feasible in practice. We propose a meta-heuristic algorithm based on variable neighborhood search approach for large sample sizes. A sensitivity analysis is also carried out in order to study the effect of mis-specification of parameter values or cost coefficients on the optimum solution. Finally, we also discuss A-, D- and T- optimum censoring schemes.