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A scheduling algorithm for medical emergency rescue aircraft trajectory based on hybrid estimation and intent inference

Author

Listed:
  • Bin Hu

    (Nanjing University of Aeronautics and Astronautics)

  • Fang Pan

    (Nanjing University of Chinese Medicine)

  • Lei Wang

    (Nanjing University of Aeronautics and Astronautics)

Abstract

This paper studies the accurate prediction of the trajectory of aircraft that is in low-lever emergency medical rescue so as to achieve effective and safe real-time scheduling. The paper acquires real-time flight state (position, speed and heading) by taking advantage of the information derived from Automatic Dependence Surveillance-broadcasting and improved A $$^{*}$$ ∗ algorithm. Then Interacting Multiple Model is introduced to predict the flight state and model of the aircraft at next point-in-time based on the current state information. Then combining the result and flight plan information together, short-term flight intent is inferred. Integrated with Interacting Multiple Model algorithm and improved Intent Inference algorithm, a trajectory prediction algorithm is put forward based on the flight state and intent inference, and finally a real time scheduling plan is coming into being. Case simulation shows the result of this algorithm is more accurate than using intent inference algorithm alone. It helps to guarantee the safety of the medical rescue aircraft and improve the efficiency of the emergency rescue.

Suggested Citation

  • Bin Hu & Fang Pan & Lei Wang, 2019. "A scheduling algorithm for medical emergency rescue aircraft trajectory based on hybrid estimation and intent inference," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 40-61, January.
  • Handle: RePEc:spr:jcomop:v:37:y:2019:i:1:d:10.1007_s10878-017-0209-y
    DOI: 10.1007/s10878-017-0209-y
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    References listed on IDEAS

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    1. Jun Yang & Jinhong Chen & Huiliang Liu & Kai Zhang & Wei Ren & Jingchen Zheng, 2013. "The Chinese national emergency medical rescue team response to the Sichuan Lushan earthquake," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(3), pages 2263-2268, December.
    2. Yinfeng Xu & Huili Zhang, 2015. "How much the grid network and rescuers’ communication can improve the rescue efficiency in worst-case analysis," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 1062-1076, November.
    3. Xi Chen & Zhiping Fan & Zhiwu Li & Xueliang Han & Xiao Zhang & Haochen Jia, 2015. "A two-stage method for member selection of emergency medical service," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 871-891, November.
    4. Bing Wang & Xingbao Han & Xianxia Zhang & Shaohua Zhang, 2015. "Predictive-reactive scheduling for single surgical suite subject to random emergency surgery," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 949-966, November.
    5. Chunyan Liu & Hejiao Huang & Hongwei Du & Xiaohua Jia, 2017. "Optimal RSUs placement with delay bounded message dissemination in vehicular networks," Journal of Combinatorial Optimization, Springer, vol. 33(4), pages 1276-1299, May.
    6. Lan Qin & Yinfeng Xu, 2017. "Fibonacci helps to evacuate from a convex region in a grid network," Journal of Combinatorial Optimization, Springer, vol. 34(2), pages 398-413, August.
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    Cited by:

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    2. Zhiguo Wang & Lufei Huang & Cici Xiao He, 0. "A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-28.

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