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JACIII Vol.15 No.8 pp. 1140-1148
doi: 10.20965/jaciii.2011.p1140
(2011)

Paper:

Multi-Objective Scheduling for Space Science Missions

Mark D. Johnston* and Mark E. Giuliano**

*Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA

**Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21219, USA

Received:
May 15, 2011
Accepted:
August 13, 2011
Published:
October 20, 2011
Keywords:
scheduling, optimization, multi-objective optimization, evolutionary algorithms
Abstract

We have developed an architecture called MUSE (Multi-User Scheduling Environment) to enable the integration of multi-objective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to reuse existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to actively engage in the optimization process, each representing one or more objectives in the optimization problem. As initial applications, we apply our approach to scheduling the James Webb Space Telescope, where three objectives aremodeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angularmomentumthat would necessitate the use of mission critical propellant to dump the momentum. As a second application area, we model aspects of the Cassini science planning process, including the trade-off between collecting data (subject to onboard recorder capacity) and transmitting saved data to Earth. A third mission application is that of scheduling the Cluster 4-spacecraft constellation plasma experiment. In this paper we describe our overall architecture and our adaptations for these different application domains. We also describe our plans for applying this approach to other science mission planning and scheduling problems in the future.

Cite this article as:
Mark D. Johnston and Mark E. Giuliano, “Multi-Objective Scheduling for Space Science Missions,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.8, pp. 1140-1148, 2011.
Data files:
References
  1. [1] K. Deb, “Multi-Objective Optimization Using Evolutionary Algorithms,” New York: John Wiley & Sons, 2001.
  2. [2] A. Abraham, L. Jain, and R. Goldberg, “Evolutionary Multiobjective Optimization,” Berlin: Springer, 2005.
  3. [3] Y. Collette and P. Siarry, “Multiobjective Optimization,” Berlin: Springer, 2003.
  4. [4] S. Kukkonen and J. Lampinen, “GDE3: The Third Evolution Step of Generalized Differential Evolution,” in The 2005 Congress on Evolutionary Computation, 2005.
  5. [5] S. Kukkonen, and J. Lampinen, “Performance assessment of generalized differential evolution 3 with a given set of constrained multi-objective test problems,” in Proc. of the Eleventh conference on Congress on Evolutionary Computation, Trondheim, Norway, 2009.
  6. [6] M. D. Johnston, “Multi-Objective Scheduling for NASA’s Deep Space Network Array,” in Int. Workshop on Planning and Scheduling for Space (IWPSS-06), Baltimore, MD: Space Telescope Science Institute, 2006.
  7. [7] M. D. Johnston, “An Evolutionary Algorithm Approach to Multi-Objective Scheduling of Space Network Communications,” Int. J. of Intelligent Automation and Soft Computing, Vol.14, pp. 367-376, 2008.
  8. [8] K. Price, R. Storn, and J. Lampinen, “Differential Evolution: A Practical Approach to Global Optimization,” Berlin: Springer, 2005.
  9. [9] R. Storn and K. Price, “Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces,” J. of Global Optimization, Vol.11, pp. 341-350, 1997.
  10. [10] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE trans. on evolutionary computation, Vol.6, No.2, pp. 182-197, 2002.
  11. [11] S. Kukkonen and K. Deb, “Improved Pruning of Non-Dominated Solutions Based on Crowding Distance for Bi-Objective Optimization Problems,” Proc. of the 2006 Congress on Evolutionary Computation (CEC 2006), 2006.
  12. [12] R. Spence, “Information Visualization,” ACM Press, Addison-Wesley, 2001.
  13. [13] J. Tidwell, “Designing Interfaces,” O’Reilly, 2005.
  14. [14] M. E. Giuliano and M. D. Johnston, “Visualization Tools For Multi-Objective Algorithms. Demonstration,” in Int. Conf. on Automated Planning and Scheduling, Toronto, Canada, 2010.
  15. [15] M. Giuliano and M. D. Johnston, “Developer Tools for Evaluating Multi-Objective Algorithms,” in 6th Int.Workshop on Planning and Scheduling in Space (IWPSS), Darmstadt, Germany, 2011.
  16. [16] R. Rager and M. Giuliano, “Evaluating Scheduling Strategies for JWST Momentum Management,” in 5th Int.Workshop on Planning and Scheduling for Space, pp. 235-243, 2006.
  17. [17] M. D. Johnston and G. E. Miller, “Spike: Intelligent Scheduling of Hubble Space Telescope Observations,” in Intelligent Scheduling, M. Zweben and M. Fox (Eds.), Morgan Kaufmann: San Mateo, pp. 391-422, 1994.
  18. [18] M. Giuliano, R. Rager, and N. Ferdous, “Towards a Heuristic for Scheduling the James Webb Space Telescope,” in ICAPS, Providence, RI., pp. 160-167, 2007.
  19. [19] M. Giuliano and M. D. Johnston, “Multi-Objective Evolutionary Algorithms for Scheduling the James Webb Space Telescope,” in Int. Conf. on Automated Planning and Scheduling (ICAPS), Sydney, Australia, 2008.
  20. [20] B. G. Paczkowski and T. L. Ray, “Cassini Science Planning Process,” in SpaceOps, 2004.
  21. [21] B. G. Paczkowski, B. Larsen, and T. L. Ray, “Managing Complexity to Maximize Science Return: Science Planning Lessons Learned from Cassini,” in Aerospace Conf., Big Sky, MT., pp. 1-14, 2009.
  22. [22] ESA. Cluster II Mission.
    Available from: http://sci.esa.int/science-e/www/area/index.cfm?fareaid=8.
  23. [23] Cluster II Wideband Data plasma wave investigation.
    Available from: http://www-pw.physics.uiowa.edu/plasma-wave/istp/cluster/.

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