To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network
in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a
robust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage is
established by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference is
calculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. In
robust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forward
to improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploitation
speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; the
exploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size.
Simulation results show that, compared with the other three methods, the planning solution obtained by this work is
more conducive to enhance the coverage rate and reduce interference and cost.