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Title: Robust modeling and planning of radio-frequency identification network in logistics under uncertainties
Authors: Xu, B.
Li, J.
Yang, Y.
Postolache, O.
Wu, H.
Keywords: Radio-frequency identification network planning
Uncertain environment
Particle swarm optimization
Issue Date: 2018
Publisher: SAGE Publications
Abstract: To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification networkin logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and arobust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage isestablished by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference iscalculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. Inrobust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forwardto improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploita-tion speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; theexploitation 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 ismore conducive to enhance the coverage rate and reduce interference and cost.
Peer reviewed: yes
DOI: 10.1177/1550147718769781
ISSN: 1550-1329
Accession number: WOS:000430022200001
Appears in Collections:IT-RI - Artigo em revista internacional com arbitragem científica

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