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Mixed-integer programming models for optimal constellation scheduling given cloud cover uncertainty

Author

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  • Valicka, Christopher G.
  • Garcia, Deanna
  • Staid, Andrea
  • Watson, Jean-Paul
  • Hackebeil, Gabriel
  • Rathinam, Sivakumar
  • Ntaimo, Lewis

Abstract

We consider the problem of scheduling observations on a constellation of remote sensors, to maximize the aggregate quality of the collections obtained. While automated tools exist to schedule remote sensors, they are often based on heuristic scheduling techniques, which typically fail to provide bounds on the quality of the resultant schedules. To address this issue, we first introduce a novel deterministic mixed-integer programming (MIP) model for scheduling a constellation of one to n satellites, which relies on extensive pre-computations associated with orbital propagators and sensor collection simulators to mitigate model size and complexity. Our MIP model captures realistic and complex constellation-target geometries, with solutions providing optimality guarantees. We then extend our base deterministic MIP model to obtain two-stage and three-stage stochastic MIP models that proactively schedule to maximize expected collection quality across a set of scenarios representing cloud cover uncertainty. Our experimental results on instances of one and two satellites demonstrate that our stochastic MIP models yield significantly improved collection quality relative to our base deterministic MIP model. We further demonstrate that commercial off-the-shelf MIP solvers can produce provably optimal or near-optimal schedules from these models in time frames suitable for sensor operations.

Suggested Citation

  • Valicka, Christopher G. & Garcia, Deanna & Staid, Andrea & Watson, Jean-Paul & Hackebeil, Gabriel & Rathinam, Sivakumar & Ntaimo, Lewis, 2019. "Mixed-integer programming models for optimal constellation scheduling given cloud cover uncertainty," European Journal of Operational Research, Elsevier, vol. 275(2), pages 431-445.
  • Handle: RePEc:eee:ejores:v:275:y:2019:i:2:p:431-445
    DOI: 10.1016/j.ejor.2018.11.043
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    References listed on IDEAS

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    1. Jean-Paul Watson & David Woodruff, 2011. "Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems," Computational Management Science, Springer, vol. 8(4), pages 355-370, November.
    2. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    3. Lewis Ntaimo, 2010. "Disjunctive Decomposition for Two-Stage Stochastic Mixed-Binary Programs with Random Recourse," Operations Research, INFORMS, vol. 58(1), pages 229-243, February.
    4. Baptiste, Philippe & Sadykov, Ruslan, 2010. "Time-indexed formulations for scheduling chains on a single machine: An application to airborne radars," European Journal of Operational Research, Elsevier, vol. 203(2), pages 476-483, June.
    5. J.M. van den Akker & C.A.J. Hurkens & M.W.P. Savelsbergh, 2000. "Time-Indexed Formulations for Machine Scheduling Problems: Column Generation," INFORMS Journal on Computing, INFORMS, vol. 12(2), pages 111-124, May.
    6. Lewis Ntaimo, 2013. "Fenchel decomposition for stochastic mixed-integer programming," Journal of Global Optimization, Springer, vol. 55(1), pages 141-163, January.
    7. William J. Wolfe & Stephen E. Sorensen, 2000. "Three Scheduling Algorithms Applied to the Earth Observing Systems Domain," Management Science, INFORMS, vol. 46(1), pages 148-166, January.
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    Cited by:

    1. Alex Elkjær Vasegaard & Ilkyeong Moon & Peter Nielsen & Subrata Saha, 2023. "Determining the pricing strategy for different preference structures for the earth observation satellite scheduling problem through simulation and VIKOR," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 945-973, September.
    2. Bahman Naderi & Rubén Ruiz & Vahid Roshanaei, 2023. "Mixed-Integer Programming vs. Constraint Programming for Shop Scheduling Problems: New Results and Outlook," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 817-843, July.

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