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Sensors Deployment in Logistics System by Genetic Invasive Weed Optimization

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Internet and Distributed Computing Systems (IDCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9864))

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Abstract

Real-time management for the production and manufacturing process of materials is necessary for the flexible manufacturing systems, RFID technique can master the processing situation of material transportation in real-time and thus improve the transportation control as well as the efficiency for the manufacturing system. We herein deploy the RFID readers and tags in the logistics system to manage the production process; and build the deployment mathematical model and the optimization strategies for RFID readers. We also propose a genetic invasive weed optimization (IWOGA) based on the invasive weed optimization (IWO) and genetic algorithm (GA) to optimize the deployment of RFID readers, IWOGA involves a comprehensive evolutionary mechanism therein with the objective of covering all the RFID tags in the whole system using the minimum number of RFID readers with the minimum working frequency. We validate the proposed optimization algorithm in comparison with IWO and GA respectively by a practical numerical example of sensors deployment in logistics system.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Yanjun Shi, Luyang Hou, Xueyan Sun & Yaohui Pan

Authors
  1. Yanjun Shi
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  2. Luyang Hou
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  3. Xueyan Sun
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  4. Yaohui Pan
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Corresponding author

Correspondence to Yanjun Shi .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology , Wuhan, China

    Wenfeng Li

  2. Central Queensland University , North Rockhampton, Queensland, Australia

    Shawkat Ali

  3. Delft University of Technology , Delft, The Netherlands

    Gabriel Lodewijks

  4. University of Calabria , Rende (CS), Italy

    Giancarlo Fortino

  5. University of Reading , Reading, United Kingdom

    Giuseppe Di Fatta

  6. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  7. CSIRO ICT , Acton, Australia

    Mukaddim Pathan

  8. ICAR-CNR, Rende (CS), Italy

    Antonio Guerrieri

  9. Wuhan University of Technology , Wuhan, China

    Qiang Wang

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© 2016 Springer International Publishing AG

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Shi, Y., Hou, L., Sun, X., Pan, Y. (2016). Sensors Deployment in Logistics System by Genetic Invasive Weed Optimization. In: Li, W., et al. Internet and Distributed Computing Systems. IDCS 2016. Lecture Notes in Computer Science(), vol 9864. Springer, Cham. https://doi.org/10.1007/978-3-319-45940-0_35

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  • DOI: https://doi.org/10.1007/978-3-319-45940-0_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45939-4

  • Online ISBN: 978-3-319-45940-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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