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An extensible modeling framework for dynamic reassignment and rerouting in cooperative airborne operations

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  • Chase C. Murray
  • Mark H. Karwan

Abstract

Unmanned aerial vehicles (UAVs), increasingly vital to the success of military operations, operate in a complex and dynamic environment, sometimes in concert with manned aircraft. We present an extensible modeling framework for the solution to the dynamic resource management (DRM) problem, where airborne resources must be reassigned to time‐sensitive tasks in response to changes in battlespace conditions. The DRM problem is characterized by diverse tasks with time windows, heterogeneous resources with fuel‐ and payload‐capacity limitations, and multiple competing objectives. We propose an integer linear programing formulation for this problem, where mathematical feasibility is guaranteed. Although motivated by airborne military operations, the proposed general modeling framework is applicable to a wide array of settings, such as disaster relief operations. Additionally, land‐ or water‐based operations may be modeled within this framework, as well as any combination of manned and unmanned vehicles. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010

Suggested Citation

  • Chase C. Murray & Mark H. Karwan, 2010. "An extensible modeling framework for dynamic reassignment and rerouting in cooperative airborne operations," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(7), pages 634-652, October.
    • Handle: RePEc:wly:navres:v:57:y:2010:i:7:p:634-652
    DOI: 10.1002/nav.20427
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    References listed on IDEAS

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    1. C Schumacher & P R Chandler & M Pachter & L S Pachter, 2007. "Optimization of air vehicles operations using mixed-integer linear programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(4), pages 516-527, April.
    2. Li, Jing-Quan & Mirchandani, Pitu B. & Borenstein, Denis, 2009. "Real-time vehicle rerouting problems with time windows," European Journal of Operational Research, Elsevier, vol. 194(3), pages 711-727, May.
    3. Lau, Hoong Chuin & Sim, Melvyn & Teo, Kwong Meng, 2003. "Vehicle routing problem with time windows and a limited number of vehicles," European Journal of Operational Research, Elsevier, vol. 148(3), pages 559-569, August.
    4. Goel, Asvin & Gruhn, Volker, 2008. "A General Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 650-660, December.
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    Cited by:

    1. Zhu, Xiaoning & Yan, Rui & Peng, Rui & Zhang, Zhongxin, 2020. "Optimal routing, loading and aborting of UAVs executing both visiting tasks and transportation tasks," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Chase Murray & Mark Karwan, 2013. "A branch‐and‐bound‐based solution approach for dynamic rerouting of airborne platforms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(2), pages 141-159, March.
    3. Xia, Jun & Wang, Kai & Wang, Shuaian, 2019. "Drone scheduling to monitor vessels in emission control areas," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 174-196.
    4. Peng, Rui, 2018. "Joint routing and aborting optimization of cooperative unmanned aerial vehicles," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 131-137.
    5. Yan Xia & Rajan Batta & Rakesh Nagi, 2017. "Controlling a Fleet of Unmanned Aerial Vehicles to Collect Uncertain Information in a Threat Environment," Operations Research, INFORMS, vol. 65(3), pages 674-692, June.

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