skip to main content
10.1145/3656766.3656839acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicbarConference Proceedingsconference-collections
research-article

A study of aeromedical emergency scheduling based on a collaborative difference algorithm improved by reinforcement learning

Published: 01 June 2024 Publication History

Abstract

With the increasing frequency of random occurrence of large natural disasters, aeromedical emergency rescue has gradually become a major hot spot in today's society. Timely and efficient rescue can save more lives in the golden moment, so more and more scholars have launched an in-depth study on the scheduling of aviation emergency resources. The purpose of this paper is to reasonably plan out the aircraft scheduling programme under large-scale disasters and make full use of the rescue materials. Therefore, this paper establishes a hierarchical collaborative scheduling model for aeromedical emergency rescue, with the objectives of minimising the total aeromedical rescue time cost, maximising the total utility of the rescue mission and maximising the satisfaction of the rescue mission. In this paper, an improved differential algorithm (RL-DE algorithm) based on the reinforcement learning framework is proposed to solve the model, and simulated data are used to prove the model and algorithm proposed in this paper, and the experimental results show that the method proposed in this paper can generate the scheduling plan better.

References

[1]
He Xin. Research on key technologies for general aviation emergency rescue resource dispatching under the influence of multiple factors [D]. Civil Aviation Flight University of China, 2022.
[2]
Seyfi-Shishavan, Seyed Amin, Elmira Farrokhizadeh, and Fatma Kutlu Gündoğdu. "A Novel Mathematical Model to Design UAV Trajectory for Search and Rescue Operations in Disaster." Intelligent and Fuzzy Techniques in Aviation 4.0: Theory and Applications, 2022: 521-542.
[3]
Mao, Sun, Shunfan He, and Jinsong Wu. "Joint UAV position optimization and resource scheduling in space-air-ground integrated networks with mixed cloud-edge computing." IEEE Systems Journal 15.3, 2020: 3992-4002.
[4]
Sharma, Shalini, and Jerry Chou. "Distributed and incremental travelling salesman algorithm on time-evolving graphs." The Journal of Supercomputing 77.10, 2021: 10896-10920.
[5]
Mohana, H., and M. Suriakala. "An enhanced prospective Jaccard similarity measure (PJSM) to calculate the user similarity score set for e-commerce recommender system." Intelligent System Design: Proceedings of Intelligent System Design: INDIA 2019. Springer Singapore, 2021.
[6]
Tang Wenguo. Research on emergency supplies dispatching for earthquake disasters[D]. Northeastern University, 2014.
[7]
Song Shengwei. Research on optimal dispatching of joint rescue supplies for marine oil spill accidents [D]. Dalian Maritime University, 2014.
[8]
Banya. Research on emergency material layout and dispatching methods under time-varying semantics [J]. Journal of Surveying and Mapping, 2018, 47(04):558.
[9]
Wang Jing. Research on multimodal transportation dispatching model of emergency supplies under uncertain conditions [D]. Huazhong University of Science and Technology, 2013.
[10]
Li Mengliang. Theoretical research on emergency logistics multi-objective planning under mixed uncertainties [D]. Beijing Jiaotong University, 2018.
[11]
Sun Keke. Research on social rescue material dispatching system and optimization under catastrophic disasters [D]. Institute of Disaster Prevention Science and Technology, 2021.

Index Terms

  1. A study of aeromedical emergency scheduling based on a collaborative difference algorithm improved by reinforcement learning

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICBAR '23: Proceedings of the 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management
    November 2023
    1156 pages
    ISBN:9798400716478
    DOI:10.1145/3656766
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 June 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICBAR 2023

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 12
      Total Downloads
    • Downloads (Last 12 months)12
    • Downloads (Last 6 weeks)2
    Reflects downloads up to 17 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media