Modeling and optimization of radio frequency identification networks for inventory management Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/w37639087

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  • Stock loss and out-of-stocks are outcomes of poorly designed inventory management systems and can lead to significant revenue losses. Inventory management systems (IMS) based on radio frequency identification (RFID) have the potential to minimize these losses if they are properly designed and deployed. However, the placement of RFID readers to support IMS is often done on a trial and error basis which is time consuming and results in less than optimal coverage. A methodology to model and optimize the design of RFID networks for IMS was developed in this research. The main objective of this methodology is to find the optimal location and number of RFID readers to ensure a desired level of coverage. Finding a solution that ensures the complete coverage of an entire facility (e.g., a warehouse) would allow an RFID network to support real-time inventory tracking and localization which can minimize shrinkage and prevent theft. Two model formulations that incorporate critical RFID network design parameters known to have an effect on the performance of RFID-based IMS were developed. When compared to prior work, the underlying assumptions that guided the construction of the model formulations make the modeling of the RFID-based IMS more realistic and applicable to warehouse environments. These important assumptions included the use of elliptical antenna coverage, the consideration of the uplink communication channel (i.e., tag-to-reader), the utilization of appropriate propagation models for the downlink and for the uplink communication channels, and the consideration of obstacles. The heuristic optimization algorithms particle swam optimization (PSO) and genetic algorithm (GA) were applied to the model formulations to search for feasible solutions that ensured appropriate coverage for the warehouse facility, with less interference and within reasonable computation time. The results show that the proposed methodology works very well with small rectangular facilities and small inverted-T facilities, but there are some limitations when applying it to large facilities.
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