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RFID-based multi-attribute logistics information processing and anomaly mining in production logistics

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  • Xiaohua Cao
  • Tiffany Li
  • Qiang Wang

Abstract

Timely collecting logistics information and finding anomalies of material supply plays a critical role in modern manufacturing systems. The problem is how to obtain multi-attribute logistics information of production logistics and build an effective approach for mining anomalies from the huge number of RFID data. The multi-attribute, randomness and various measure units of logistics states further aggravate the problem. In this paper, a novel RFID-based logistics information processing approach is proposed. Firstly, the state features of production logistics is discussed from multi-attribute perspectives including time, location, quantities, sequence and path, and a set of calculating models is set up to process RFID data for getting multi-attribute state data. Furthermore, in case of the randomness and various measure units of state data, a similarity model is presented to unify measure units of state data, and a clustering approach is proposed to divide the huge number of RFID data into different clusters with high close degree for finding out anomalies. Lastly, the experimental results show that the proposed approach can efficiently find out more than 90% of anomalies among production logistics.

Suggested Citation

  • Xiaohua Cao & Tiffany Li & Qiang Wang, 2019. "RFID-based multi-attribute logistics information processing and anomaly mining in production logistics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(17), pages 5453-5466, September.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:17:p:5453-5466
    DOI: 10.1080/00207543.2018.1526421
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    Cited by:

    1. Zhitao Xu & Adel Elomri & Roberto Baldacci & Laoucine Kerbache & Zhenyong Wu, 2024. "Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective," Annals of Operations Research, Springer, vol. 338(2), pages 1359-1401, July.
    2. Shaohua Huang & Yu Guo & Nengjun Yang & Shanshan Zha & Daoyuan Liu & Weiguang Fang, 2021. "A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1845-1861, October.

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