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An intelligent logistics service system for enhancing dispatching operations in an IoT environment

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  • Wang, Jianxin
  • Lim, Ming K.
  • Zhan, Yuanzhu
  • Wang, XiaoFeng

Abstract

This paper proposed an IoT-based intelligent logistics dispatching system, which enables dynamic coordination between customers, order-picking robots and cloud technology. This system includes three layouts: the framework structure of intelligent dispatching platform based on an IoT environment; the multi-objective optimization model to achieve the efficient dynamic coordination between customers, order-picking robots and the cloud technology; the core two-level algorithm, which comprises of Dijkstra’s algorithm and ant colony algorithm that supports the intelligent dispatching operations. This research shows its ability to efficiently coordinate the dispatching operations through IoT technology to enhance customer satisfaction, and outperforms the traditional dispatching methodologies.

Suggested Citation

  • Wang, Jianxin & Lim, Ming K. & Zhan, Yuanzhu & Wang, XiaoFeng, 2020. "An intelligent logistics service system for enhancing dispatching operations in an IoT environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:transe:v:135:y:2020:i:c:s1366554519309767
    DOI: 10.1016/j.tre.2020.101886
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    7. Guangmei Cao & Yuesen Wang & Honghu Gao & Hao Liu & Haibin Liu & Zhigang Song & Yuqing Fan, 2023. "Coordination Decision-Making for Intelligent Transformation of Logistics Services under Capital Constraint," Sustainability, MDPI, vol. 15(6), pages 1-25, March.
    8. Dejian Yu & Zhaoping Yan, 2022. "Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4251-4274, July.
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    10. Yu, Chao & Li, Haiying & Xu, Xinyue & Liu, Jun, 2020. "Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
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    15. Chen, Li-Ming & Chang, Wei-Lun, 2021. "Supply- and cyber-related disruptions in cloud supply chain firms: Determining the best recovery speeds," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).

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