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Three Scheduling Algorithms Applied to the Earth Observing Systems Domain

Author

Listed:
  • William J. Wolfe

    (Department of Computer Science and Engineering, University of Colorado at Denver, Denver, Colorado 80217)

  • Stephen E. Sorensen

    (Hughes Information Technology Systems, Aurora, Colorado 80011)

Abstract

This paper describes three approaches to assigning tasks to earth observing satellites (EOS). A fast and simple priority dispatch method is described and shown to produce acceptable schedules most of the time. A look ahead algorithm is then introduced that outperforms the dispatcher by about 12% with only a small increase in run time. These algorithms set the stage for the introduction of a genetic algorithm that uses job permutations as the population. The genetic approach presented here is novel in that it uses two additional binary variables, one to allow the dispatcher to occasionally skip a job in the queue and another to allow the dispatcher to occasionally allocate the worst position to the job. These variables are included in the recombination step in a natural way. The resulting schedules improve on the look ahead by as much as 15% at times and 3% on average. We define and use the "window-constrained packing" problem to model the bare bones of the EOS scheduling problem.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormnsc:v:46:y:2000:i:1:p:148-166
    DOI: 10.1287/mnsc.46.1.148.15134
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    Citations

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    Cited by:

    1. Kaiping Luo, 2015. "Space‐Based Infrared Sensor Scheduling with High Uncertainty: Issues and Challenges," Systems Engineering, John Wiley & Sons, vol. 18(1), pages 102-113, January.
    2. Bekki, Özgün BarIs & Azizoglu, Meral, 2008. "Operational fixed interval scheduling problem on uniform parallel machines," International Journal of Production Economics, Elsevier, vol. 112(2), pages 756-768, April.
    3. Philippe Monmousseau, 2021. "Scheduling of a Constellation of Satellites: Creating a Mixed-Integer Linear Model," Journal of Optimization Theory and Applications, Springer, vol. 191(2), pages 846-873, December.
    4. Orhan Karasakal & Levent Kandiller & Nur Evin Özdemirel, 2011. "A branch and bound algorithm for sector allocation of a naval task group," Naval Research Logistics (NRL), John Wiley & Sons, vol. 58(7), pages 655-669, October.
    5. Mahalec, Vladimir & Chen, Yingwu & Liu, Xiaolu & He, Renjie & Sun, Kai, 2015. "Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolutionAuthor-Name: Chen, Yingguo," European Journal of Operational Research, Elsevier, vol. 242(1), pages 10-20.
    6. 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.
    7. Bianchessi, Nicola & Cordeau, Jean-Francois & Desrosiers, Jacques & Laporte, Gilbert & Raymond, Vincent, 2007. "A heuristic for the multi-satellite, multi-orbit and multi-user management of Earth observation satellites," European Journal of Operational Research, Elsevier, vol. 177(2), pages 750-762, March.
    8. Türsel Eliiyi, Deniz & Azizoglu, Meral, 2011. "Heuristics for operational fixed job scheduling problems with working and spread time constraints," International Journal of Production Economics, Elsevier, vol. 132(1), pages 107-121, July.
    9. Zhang Ye & Hu Xiaoxuan & Zhu Waiming & Jin Peng, 2018. "Solving the Observing and Downloading Integrated Scheduling Problem of Earth Observation Satellite with a Quantum Genetic Algorithm," Journal of Systems Science and Information, De Gruyter, vol. 6(5), pages 399-420, October.
    10. Jie Chun & Wenyuan Yang & Xiaolu Liu & Guohua Wu & Lei He & Lining Xing, 2023. "Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem," Mathematics, MDPI, vol. 11(19), pages 1-20, September.
    11. Shai Krigman & Tal Grinshpoun & Lihi Dery, 2024. "Scheduling of Earth observing satellites using distributed constraint optimization," Journal of Scheduling, Springer, vol. 27(5), pages 507-524, October.
    12. Jang, Jinbong & Choi, Jiwoong & Bae, Hee-Jin & Choi, In-Chan, 2013. "Image collection planning for KOrea Multi-Purpose SATellite-2," European Journal of Operational Research, Elsevier, vol. 230(1), pages 190-199.
    13. J-F Cordeau & G Laporte, 2005. "Maximizing the value of an Earth observation satellite orbit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 962-968, August.
    14. Chen, Xiaoyu & Reinelt, Gerhard & Dai, Guangming & Spitz, Andreas, 2019. "A mixed integer linear programming model for multi-satellite scheduling," European Journal of Operational Research, Elsevier, vol. 275(2), pages 694-707.
    15. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    16. Huilong Fan & Zhan Yang & Shimin Wu & Xi Zhang & Jun Long & Limin Liu, 2021. "An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks," Mathematics, MDPI, vol. 9(24), pages 1-23, December.

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    Keywords

    Scheduling; Algorithms; Genetic;
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