Adaptive product tracking in RFID-enabled large-scale supply chain

Jong Myoung Ko; Choonjong Kwak; Youngho Cho; Chang Ouk Kim

doi:10.1016/j.eswa.2010.07.077

Adaptive product tracking in RFID-enabled large-scale supply chain

Jong Myoung Ko, Choonjong Kwak, Youngho Cho, Chang Ouk Kim

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

34 Citations (Scopus)

Abstract

The Radio Frequency Identification (RFID) technology is gradually being adopted and deployed for product flow management in the supply chain. In order to track RFID-tagged products efficiently in the RFID-enabled, large-scale supply chain, this paper first presents the design of a product tracking system that can collaborate with the EPC Network, a suite of network services for RFID data management in the supply chain. Next, we explain a product monitoring procedure that is performed by comparing the actual path of a product with its planned path. Finally, we develop an adaptive product search algorithm based on a reinforcement learning technique to efficiently locate a product deviated from its planned path. Experiment results are provided to show the performance of the search algorithm.

Original language	English
Pages (from-to)	1583-1590
Number of pages	8
Journal	Expert Systems with Applications
Volume	38
Issue number	3
DOIs	https://doi.org/10.1016/j.eswa.2010.07.077
Publication status	Published - 2011 Mar

All Science Journal Classification (ASJC) codes

Engineering(all)
Computer Science Applications
Artificial Intelligence

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abstract = "The Radio Frequency Identification (RFID) technology is gradually being adopted and deployed for product flow management in the supply chain. In order to track RFID-tagged products efficiently in the RFID-enabled, large-scale supply chain, this paper first presents the design of a product tracking system that can collaborate with the EPC Network, a suite of network services for RFID data management in the supply chain. Next, we explain a product monitoring procedure that is performed by comparing the actual path of a product with its planned path. Finally, we develop an adaptive product search algorithm based on a reinforcement learning technique to efficiently locate a product deviated from its planned path. Experiment results are provided to show the performance of the search algorithm.",

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