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Research of MIP-HCO Model Based on k-Nearest Neighbor and Branch-and-Bound Algorithms in Aerospace Emergency Launch Missions

Author

Listed:
  • Xiangzhe Li

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

  • Feng Zhan

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

  • Jinqing Huang

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

  • Yan Chen

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
    Digital Guangxi lntelligent Infrastructure Joint Innovation Laboratory, Nanning 530000, China)

Abstract

This study proposes a mixed-integer programming-based hierarchical collaborative optimization (MIP-HCO) model to optimize the scheduling and execution of emergency launch missions, ensuring rapid response and performance maximization under constrained time and resources. The key innovation lies in integrating k-Nearest Neighbor (KNN) with Branch and Bound (B&B) to enhance computational efficiency and global optimality. The first layer constructs a spatiotemporal optimization model, considering launch sites, storage proximity, and process duration. The B&B algorithm solves mission scheduling, while a dynamic adjustment strategy optimizes launch vehicle reutilization. The second layer refines mission selection based on contribution assessment and re-optimizes scheduling using integer programming. KNN classification approximates scheduling quality, reducing B&B complexity and accelerating convergence. Results from simulation data and experimental simulations confirm that the KNN + B&B hybrid strategy optimizes scheduling efficiency, enabling launch systems to respond swiftly under emergencies while maximizing mission effectiveness.

Suggested Citation

  • Xiangzhe Li & Feng Zhan & Jinqing Huang & Yan Chen, 2025. "Research of MIP-HCO Model Based on k-Nearest Neighbor and Branch-and-Bound Algorithms in Aerospace Emergency Launch Missions," Mathematics, MDPI, vol. 13(10), pages 1-30, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:10:p:1652-:d:1658463
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    References listed on IDEAS

    as
    1. Chuanhu Niu & Aijuan Li & Xin Huang & Wei Li & Chuanyan Xu, 2021. "Research on Global Dynamic Path Planning Method Based on Improved A Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, August.
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