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Multi-objective optimization of space station short-term mission planning

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Abstract

This paper studies the multi-objective optimization of space station short-term mission planning (STMP), which aims to obtain a mission-execution plan satisfying multiple planning demands. The planning needs to allocate the execution time effectively, schedule the on-board astronauts properly, and arrange the devices reasonably. The STMP concept models for problem definitions and descriptions are presented, and then an STMP multi-objective planning model is developed. To optimize the STMP problem, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted and then improved by incorporating an iterative conflict-repair strategy based on domain knowledge. The proposed approach is demonstrated by using a test case with thirty-five missions, eighteen devices and three astronauts. The results show that the established STMP model is effective, and the improved NSGA-II can successfully obtain the multi-objective optimal plans satisfying all constraints considered. Moreover, through contrast tests on solving the STMP problem, the NSGA-II shows a very competitive performance with respect to the Strength Pareto Evolutionary Algorithm II (SPEA-II) and the Multi-objective Particle Swarm Optimization (MOPSO).

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References

  1. Popov A. Mission planning on the international space station program, concepts and systems. In: Proceedings of IEEE Aerospace Conference, Big Sky, USA, 2003

    Google Scholar 

  2. Leuttgens R, Volpp J. Operations planning for the international space station. ESA bulletin 94, 1998, 94: 1–7

    Google Scholar 

  3. Jorgensen C A. International space station evolution data book. NASA 2000 Technical Report, Baseline Design-Revision A, 2000

    Google Scholar 

  4. Cesta A, Benedictis R, Orlandini A, et al. Integrating planning and scheduling in the ISS fluid science laboratory domain. In: Proceedings of 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Amsterdam, Holland, 2013

    Google Scholar 

  5. Grogan P, Yue H, Weck O. Space logistics modeling and simulation analysis using spacenet: Four application cases. In: AIAA SPACE 2011 Conference & Exposition, Long Beach, California, 2011

    Google Scholar 

  6. Ho K, Green J, Weck O. Integrated framework for the design of crewed space habitats and their supporting logistics system. In: AIAA Space 2012 Conference and Exposition, Pasadena, CA, 2012

    Google Scholar 

  7. Kortenkamp D. A day in an astronaut’s life: Reflections on advanced planning and scheduling technology. IEEE Intell Syst, 2003, 18: 8–11

    Article  Google Scholar 

  8. Russell J, Klaus D, Mosher T. Applying analysis of international space station crew-time utilization to mission design. J Spacecraft Rockets, 2006, 43: 130–136

    Article  Google Scholar 

  9. Clement B J, Barreiro J, Iatauro M J, et al. Spatial planning for international space station crew operations. In: Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space, Hokkaido, Japan, 2010

    Google Scholar 

  10. J Barreiro, Jones G, Schaffer S. Peer-to-Peer Planning for Space Mission Control. Big Sky, Montana: Big Sky Resort, 2009. 4032–4041

    Google Scholar 

  11. Lin K P, Luo Y Z, Zhang J, et al. Space station overall mission planning using decomposition approach. Aerosp Sci Technol, 2014, 33: 26–39

    Article  Google Scholar 

  12. Lin K P, Luo Y Z, Zhang J, et al. Planning for space station long-duration orbital mission under multi-constraints. Sci China Tech Sci, 2013, 56: 1075–1085

    Article  Google Scholar 

  13. Saint R. Lessons learned in developing an international planning software system. In: Proceedings of the 7th International Conference on Space Operations, Houston, Texas, USA, 2002

    Google Scholar 

  14. Hagopian J, Howell E. Architecture for payload planning system (PPS) software distribution. In: Proceedings of the Space Programs and Technologies Conference, Huntsville, USA, 1995

    Google Scholar 

  15. Zagreev B, Repchenkov R, Chikirev V. A decision support system for research program scheduling on the Russian segment of the ISS. In: Proceedings of the 64th International Astronautical Congress, Beijing, China, 2013

    Google Scholar 

  16. Zoeschinger G A, Wickler M, Koehler A, et al. A planning system for payload and system planning of the columbus module on ISS. In: Proceedings of the 8th International Conference on Space Operations, Montreal, Canada, 2004

    Google Scholar 

  17. Ogo H, Maeda E. Implementation status for JEM logistics and maintenance operations system. In: Proceedings of the 50th International Astronautical Congress, Amsterdam, Holland, 1999

    Google Scholar 

  18. Kobayashi K, Shimizu M, Satoh Y, et al. JEM operations control system design for utilization of JEM Ter-orbit communication system. In: Proceedings of the 5th International Conference on Space Operations, Tokyo, Japan, 1998

    Google Scholar 

  19. Maldague P F, Ko A Y. JIT planning: An approach to autonomous scheduling for space missions. In: IEEE Aerospace Conference, Snowmass, Aspen, USA, 1999

    Google Scholar 

  20. Chien S, Sherwood R, Tran D, et al. Using autonomy flight software to improve science return on earth observing one. J Aero Comput Inform Comm, 2005, 2: 196–216

    Article  Google Scholar 

  21. Kim I J, Lee S, Jung W C, et al. Preliminary design for satellite mission operation from mission request to command planning. In: IEEE Advanced Communication Technology, Phoenix Park, Korea, 2006

    Google Scholar 

  22. Kucinskis F N, Ferreira M G V. Planning on-board satellites for the goal-based operations for space missions. IEEE Lat Am T, 2013, 11: 1110–1120

    Article  Google Scholar 

  23. Cesta A, Cortellessa G, Fratini S, et al. Developing an end-to-end planning application from a timeline representation framework. In: Proceedings of the twenty-first Conference on innovative Applications of Artificial Intelligence, Pasadena, California, USA, 2009

    Google Scholar 

  24. Frank J, Jonsson A. Constraint-based attribute and interval planning. J Constraints, 2003, 8: 339–364

    Article  MathSciNet  MATH  Google Scholar 

  25. Chien S, Rabideau G, Knight R, et a1. ASPEN-automating spacemission operations using automated planning and scheduling. In: Proceedings of the 6th International Conference on Space Operations, Toulouse, France, 2000

    Google Scholar 

  26. Rabideau G, Chien S, Willis G, et a1. Using iterative repair to automate planning and scheduling of shuttle payload operations. In: Proceedings of the 16th National Conference on Artificial Intelligence and 11th Conference on Innovative Applications of Artificial Intelligence, Orlando, Florida, 1999

    Google Scholar 

  27. Wang H F, Li X Z, Liu Y R, et al. Summary of intelligent algorithms in planning & scheduling of Earth Observation Satellite. In: Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference, Xiamen, China, 2010

    Google Scholar 

  28. Zhang J, Luo Y Z, Tang G J. Hybrid planning for LEO long-duration multi-spacecraft rendezvous mission. Sci China Tech Sci, 2012, 55: 233–243

    Article  Google Scholar 

  29. Globus A, Crawford J, Lohn J, et al. Scheduling earth observing satellites with evolutionary algorithms. In: Proceeding of International Conference on Space Mission Changes for Information Technology, Pasadena, California, USA, 2003

    Google Scholar 

  30. Wolfe W, Sorensen S. Three scheduling algorithms applied to the earth observing systems domain. Manage Sci, 2000, 46: l48–168

    Article  Google Scholar 

  31. Zhang J, Tang G J, Luo Y Z, et al. Hybrid multi-objective optimization for concurrent activities consolidating two docked spacecraft. Int J Syst Sci, 2015, 46: 2905–2917

    Article  MathSciNet  Google Scholar 

  32. Luo Y Z, Zhou L N. Asteroid rendezvous mission design using multiobjective particle swarm optimization. Math Probl Eng, 2014, doi: 10.1155/2014/823659

    Google Scholar 

  33. Luo Y Z, Tang G J, Lei Y J, Optimal multi-objective linearized impulsive rendezvous, J Guid Control Dynam, 2007, 30: 383–389

  34. Wang Y Z. Launching manned space station project and promoting the development of China’s manned space engineering (in Chinese). Manned Spacefl, 2011, 17: 1–4

    Google Scholar 

  35. Goldberg D E. Genetic Algorithm in Search. Optimization and Machine Learning. NY: Addison-Wesley, New York, USA. 1989

    Google Scholar 

  36. Srinivas N, Deb K. Multiobjective optimization using nondominated sorting genetic algorithms. IEEE T Evolut Comput, 1995, 2: 221–248

    Article  Google Scholar 

  37. Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE T Evolut Comput, 2002, 6: 182–197

    Article  Google Scholar 

  38. Javadia M S, Sanieia M, Mashhadib H R. An augmented NSGA-II technique with virtual database to solve the composite generation and transmission expansion. J Exp Theor Artif In, 2014, 26: 211–234

    Article  Google Scholar 

  39. Zitzler E, Thiele L, Laumanns M, et al. Performance assessment of multiobjective optimizers: An analysis and review. IEEE T Evolut Comput, 2003, 7, 117–132

    Article  Google Scholar 

  40. Rabiee M, Zandieh M, Ramezani P. Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. Int J Prod Res, 2012, 50: 7327–7342

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, 410073, China

    HuiJiao Bu, Jin Zhang & YaZhong Luo

  2. Chinese Manned Space Engineering Office, Beijing, 100029, China

    JianPing Zhou

Authors
  1. HuiJiao Bu
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  2. Jin Zhang
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  3. YaZhong Luo
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  4. JianPing Zhou
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Correspondence to YaZhong Luo.

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Bu, H., Zhang, J., Luo, Y. et al. Multi-objective optimization of space station short-term mission planning. Sci. China Technol. Sci. 58, 2169–2185 (2015). https://doi.org/10.1007/s11431-015-5851-y

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