Optimal Experimental Designs for Clustered Read-Out Data of Reliability Tests Via Particle Swarm Optimization

Clustered data from a product’s reliability experiments may arise from many sources of heterogeneity in practice such as different test units suppliers, pieces of equipment, and operators. It is of interest for reliability practitioners to investigate how to plan such experiments with the aim of achieving the greatest test efficiency. In this paper, we develop optimal experimental designs of accelerated life tests under the interval censoring scheme when the obtained read-out observations are expected to be correlated and thereby clustered induced by two suppliers effects. The optimality condition is sought such that the design produces the minimum prediction variance at the product’s use condition. The correlated read-out data is modeled by a binomial generalized linear mixed model, and the Fisher information matrix is derived based on marginalized quantities in the covariance matrix. The particle swarm optimization is applied to search the design space and find the optimal solution with numerical stability under the complex nonlinear objective function. The optimal design shows the balanced test units allocations over two suppliers for each test condition. However, it is also found that degrees of such balance could be vary depending on the location of test condition, which allows flexible test units allocations for some test conditions when the number of test units from suppliers are imbalanced