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An optimal routing policy for unmanned aerial vehicles (analytical and cross-entropy simulation approach)

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  • Edward Ianovsky
  • Joseph Kreimer

Abstract

We consider a real-world problem of military intelligence unit equipped with identical unmanned aerial vehicles producing real-time imagery and responsible for heterogeneous regions (with requests of real-time jobs) required to be under nonstop surveillance. Under certain assumptions these real-time systems can be treated as queueing networks. The use of the system involving unmanned aerial vehicles relies on the principle of availability, namely on its ability to process the maximal portion of real-time tasks. We show that even very large number of vehicles does not guarantee the maximal system availability without proper choice of routing probabilities. We compute analytically (for exponentially distributed service and maintenance times) and via simulation using Cross-Entropy method (for generally distributed service times) optimal routing probabilities which maximize system availability. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • Edward Ianovsky & Joseph Kreimer, 2011. "An optimal routing policy for unmanned aerial vehicles (analytical and cross-entropy simulation approach)," Annals of Operations Research, Springer, vol. 189(1), pages 215-253, September.
    • Handle: RePEc:spr:annopr:v:189:y:2011:i:1:p:215-253:10.1007/s10479-009-0609-1
    DOI: 10.1007/s10479-009-0609-1
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    References listed on IDEAS

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    1. Reuven Rubinstein, 1999. "The Cross-Entropy Method for Combinatorial and Continuous Optimization," Methodology and Computing in Applied Probability, Springer, vol. 1(2), pages 127-190, September.
    2. Pieter-Tjerk de Boer, 2005. "Rare-Event Simulation of Non-Markovian Queueing Networks Using a State-Dependent Change of Measure Determined Using Cross-Entropy," Annals of Operations Research, Springer, vol. 134(1), pages 69-100, February.
    3. M A J Smith & R Dekker & J Kos & J A M Hontelez, 2001. "The availability of unmanned air vehicles: a post-case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(2), pages 161-168, February.
    4. Lian, Zhaotong & Deshmukh, Abhijit, 2006. "Performance prediction of an unmanned airborne vehicle multi-agent system," European Journal of Operational Research, Elsevier, vol. 172(2), pages 680-695, July.
    5. Ianovsky, Edward & Kreimer, Joseph, 2003. "Optimization of real-time multiserver system with two different channels and shortage of maintenance facilities," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 63(6), pages 615-627.
    6. Joseph Kreimer & Abraham Mehrez, 1994. "Optimal Real-Time Data Acquisition and Processing by a Multiserver Stand-by System," Operations Research, INFORMS, vol. 42(1), pages 24-30, February.
    7. Krishna Chepuri & Tito Homem-de-Mello, 2005. "Solving the Vehicle Routing Problem with Stochastic Demands using the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 153-181, February.
    8. Kreimer, Joseph & Mehrez, Abraham, 1993. "An optimal operation policy for real-time n-server stand-by systems involving preventive maintenance," European Journal of Operational Research, Elsevier, vol. 69(1), pages 50-54, August.
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