UAV Mission Planning: From Robust to Agile

  • Lanah Evers
  • Ana Isabel Barros
  • Herman Monsuur
  • Albert Wagelmans
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 56)


Unmanned Aerial Vehicles (UAVs) are important assets for information gathering in Intelligence Surveillance and Reconnaissance (ISR) missions. Depending on the uncertainty in the planning parameters, the complexity of the mission and its constraints and requirements, different planning methods might be preferred. The first two planning approaches that we will discuss, deal with uncertainty in fuel consumption of the UAV. The third planning approach is designed for an even more uncertain and dynamic situation in which travel and recording times are stochastic, time windows are associated to target locations and new targets become of interest during the flight of the UAV. As such, the proposed approaches gradually move from robust to agile as the uncertainty and dynamicity in the problems increases.


Unmanned aerial vehicles (UAVs) Intelligence surveillance and reconnaissance (ISR) Traveling salesman problem (TSP) Robust orienteering problem (ROP) Profit shortage Two-stage orienteering problem (TSOP) Sample average approximation (SAA) Weighted location coverage (WLC) Maximum coverage stochastic orienteering problem with time windows (MCS-OPTW) 


  1. Barros, A.I, Monsuur, H.: New trends in military planning: Agility and robustness. Proceedings of the 1st conference of the International Society of Military Sciences, 2009Google Scholar
  2. Ben-Tal, A., Ghaoui, L.El, Nemirovski, A.: Robust Optimization. Princeton UniversityPress, Princeton (2009)Google Scholar
  3. Evers, L., Dollevoet, T., Barros, A.I., Monsuur, H.: Robust UAV mission planning. Ann. Oper. Res. (2012). 222(1):293–315 (2014). doi: 10.1007/s10479–012–1261–8Google Scholar
  4. Evers, L., Glorie, K., van der Ster, S., Barros, A.I., Monsuur, H.: A two-stage approach to the orienteering problem with stochastic weights. Comput. Oper. Res. 43, 248–260 (2014a)Google Scholar
  5. Evers, L., Barros, A.I., Monsuur, H., Wagelmans, A.P.M.: Online stochastic UAV mission planning with time windows and time-sensitive targets. Eur. J. Oper. Res. 238(1):348–362 (2014). doi:10.1016/j.ejor.2014.03.014Google Scholar
  6. IBM. IBM ILOG CPLEX V12.1 user manual for CPLEX (2009)Google Scholar
  7. Mufalli, F., Batta, R., Nagi, R.: Simultaneous sensor selection and routing of unmanned aerial vehicles for complex mission plans. Comput. Oper. Res. 39(11):2787–2799 (2012)CrossRefGoogle Scholar
  8. Norkin, V.I., Pug, G.C., Ruszcynski, A.: A branch and bound method for stochastic global optimization. Math. Program. 83, 425–450 (1998)Google Scholar
  9. Royset, J.O., Reber, D.N.: Optimized routing of unmanned aerial systems for the interdiction of improvised explosive devices. Mil. Oper. Res. 14(4):5–19 (2009)CrossRefGoogle Scholar
  10. Solomon, M.: Algorithms for the vehicle routing and scheduling problem with time window constraints. Oper. Res. 35, 254–265 (1987)CrossRefGoogle Scholar
  11. Tsiligirides, T.: Heuristic methods applied to orienteering. J. Oper. Res. Soc. 35, 797–809 (1984)CrossRefGoogle Scholar
  12. Vansteenwegen, P., Souffria, W., van Oudheusden, D.: The orienteering problem: A survey. Eur. J. Oper. Res. 209(1):1–10 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lanah Evers
    • 1
  • Ana Isabel Barros
    • 2
    • 3
  • Herman Monsuur
    • 3
  • Albert Wagelmans
    • 4
  1. 1.QuintiqDen BoschThe Netherlands
  2. 2.TNOZeistThe Netherlands
  3. 3.Faculty of Military SciencesNetherlands Defence AcademyBredaThe Netherlands
  4. 4.Econometric InstituteErasmus University RotterdamRotterdamThe Netherlands

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