Advertisement

Integrated agile observation satellite scheduling problem considering different memory environments: a case study

Abstract

This paper presents an experimental study on an integrated agile earth observation satellite (AEOS) scheduling problem involving the satellite memory environments of the partially erasable memory (PEM) and the holistically erasable memory (HEM). The integrated AEOS scheduling problem simultaneously considers the satellite observation and transmission events, in which the onboard memory functions as a very important connective resource. To address the memory constraints in the AEOS scheduling problem, an integrated AEOS scheduling model that is suitable for both the PEM and HEM environments is proposed in this paper. Based on commonly used construction heuristic and meta-heuristics, two hybrid approaches, Tabu-simulated annealing (TSA) and Tabu late acceptance (TLA) algorithms, are adopted to solve this problem. The highlights in this paper are the formulation of novel adaptive memory constraints for the AEOS scheduling and the quantitatively scheduling comparison of separated and integrated modeling methods. Experimental results indicate that (1) the memory environment has a direct influence on the AEOS integrated scheduling results, where the PEM environment sufficiently utilizes memory resources and advances the efficiency of the AEOS a lot. (2) The integrated scheduling method enables the reduction in resource consumption and obtains a better result than the separated scheduling method, especially in the HEM environment, (3) and the hybrid meta-heuristics TLA and TSA that show better overall performance are suggested for addressing the studied AEOS integrated scheduling problem.

This is a preview of subscription content, log in to check access.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Abbreviations

AEOS:

Agile earth observation satellite

HEM:

Holistically erasable memory

PEM:

Partially erasable memory

SA:

Simulated annealing algorithm

TSA:

Hybrid Tabu-simulated annealing algorithm

OTW:

Observation time window

TTW:

Transmission time window

TS:

Tabu search algorithm

LA:

Late acceptance algorithm

TLA:

Hybrid Tabu late acceptance algorithm

i, i′ :

Index of a unit task

j, j′ :

Index of an AEOS

k, k′ :

Index of an orbit

l, l′ :

Index of an OTW/TTW in orbits

l T, l L :

Length of Tabu late list in the TS/LA algorithm

m, m′ :

Index of a moment in OTW/TTWs

f(R):

Objective function of the scheduling problem

T 0 :

Initial temperature in the SA algorithm

r i :

Unit task

R 0 :

Uninitialized solution in algorithms

R cur :

The current solution whose neighborhood is explored in the TS algorithm

R′ :

The neighborhood solution in algorithms

s j :

AEOS

s ij :

AEOS available to task ri

o ijk :

Orbit of AEOS sij

otwijkl :

OTW in oijk

ttwijkl :

TTW in oijk

ttwjn :

TTW in AEOS sj

oetijkl :

End-time of otwijkl

otrans(\(r_{i} ,r_{{i^{{\prime }} }}\)):

Time-dependent function calculating the transition time between observation tasks

Δ:

Function calculating the transition time of AEOS operating modes

change(R, i):

Function alternating the decision variable of ri

p i :

Priority of unit task ri

m i :

Observation data storage of unit task ri

oei :

Observation electricity of unit task ri

tei :

Transmission electricity of unit task ri

α :

Conserving coefficient for storage

oli :

Observation duration of unit task ri

tli :

Transmission duration of unit task ri

x ijklm :

Whether the observation event of unit task ri is executed at moment m within OTW otwijkl in orbit oijk of AEOS sij

y ijklm :

Whether the transmission event of unit task ri is executed at moment m within TTW ttwijkl in orbit oijk of AEOS sij

z jn :

Whether the AEOS erases its memory when it completes the transmission event of unit task ri (HEM environment only)

I :

Total unit task number

J :

Total AEOS number

J i :

AEOS number available to task ri

K j :

Total orbit number of AEOS sj

K ij :

Orbit number of AEOS sij

OLijk :

OTW number in orbit oijk

L T, L L :

Tabu late list in the TS/LA algorithm

TLijk :

TTW number in orbit oijk

T j :

Total TTW number of AEOS sj

T max :

Maximum computing time in algorithms

R :

Total set of unit tasks/a solution in algorithms

R 1 :

Initialized solution in algorithms

R * :

Is the best solution found in the neighborhood of Rcur in the TS algorithm

N T :

Neighborhood exploring times in the TS algorithm

S :

Total set of AEOSs

S i :

Set of sij

O ij :

Set of oijk

OTWijk :

Set of otwijkl

TTWijk :

Set of ttwijkl

TTWj :

Set of ttwjn

tetijkl :

End-time of ttwijkl

ttrans(\(r_{i} ,r_{{i^{{\prime }} }}\)):

Time-dependent function calculating the transition time between transmission tasks

quantity(r i):

Time-dependent function calculating the observation quantity of task ri

swap(R, i, j):

Function swapping the decision variables of ri and rj

q i :

Requested observation quality of unit task ri

M j :

Onboard data volume of AEOS sj

E jk :

Total electricity of orbit oijk

β :

Conserving coefficient for electricity

ohi :

Observation start time of unit task ri

thi :

Transmission start time of unit task ri

References

  1. 1.

    Beaumet G, Verfaillie G, Charmeau MC (2011) Feasibility of autonomous decision making on board an agile earth-observing satellite. Comput Intell 27(1):123–139

  2. 2.

    Zhou Y, Yan Y, Huang X, Kong LJ (2015) Mission planning optimization for multiple geosynchronous satellites refueling. Adv Space Res 56(11):2612–2625

  3. 3.

    Tangpattanakul P, Jozefowiez N, Lopez P (2015) A multi-objective local search heuristic for scheduling Earth observations taken by an AEOS. Eur J Oper Res 245(2):542–554

  4. 4.

    Chu XG, Chen YN, Tan YJ (2017) An anytime branch and bound algorithm for agile earth observation satellite onboard scheduling. Adv Space Res 60(9):2077–2090

  5. 5.

    Rehman SZU, Sultan Q, Khan MA, Omer M (2018) Analytical and experimental investigation of satellite thruster vacuum performance employing supersonic ejectors. J Braz Soc Mech Sci Eng 40:19. https://doi.org/10.1007/s40430-017-0940-4

  6. 6.

    Calvo D, Avilés T, Lapuerta V, Laverón-Simavilla A (2016) Fuzzy attitude control for a nanosatellite in low Earth orbit. Expert Syst Appl 58:102–118

  7. 7.

    Wolfe WJ, Sorensen SE (2000) Three scheduling algorithms applied to the Earth observing systems domain. Manag Sci 46(1):148–166

  8. 8.

    Bianchessi N, Righini G (2008) Planning and scheduling algorithm for the COSMO-SkyMed constellation. Aerosp Sci Technol 12(7):535–544

  9. 9.

    Lemaître M, Verfaillie G, Jouhaud F, Lachiver JM, Bataille N (2002) Selecting and scheduling observations of AEOSs. Aerosp Sci Technol 6(5):367–381

  10. 10.

    Lemaître M, Verfaillie G, Jouhaud F (2000) How to manage the new generation of agile earth observation satellites. In: Proceedings of the 6th international SpaceOps symposium (space operations), Toulouse, France, pp 1–8

  11. 11.

    Cordeau JF, Laporte G (2005) Maximizing the value of an Earth observation satellite orbit. J Oper Res Soc 56(8):962–968

  12. 12.

    Bianchessi N, Cordeau JF, Descrosiers J, Laporte G, Raymond V (2007) A heuristic for the multi-satellite, multi-orbit and multi-user management of Earth observation satellites. Eur J Oper Res 177(2):750–762

  13. 13.

    Wu GH, Liu J, Ma MH, Qiu DS (2013) A two-phase scheduling method with the consideration of task clustering for earth observing satellites. Comput Operat Res 40(7):1884–1894

  14. 14.

    Liu XL, Laporte G, Chen YW, He RJ (2017) An adaptive large neighborhood search meta-heuristic for AEOS scheduling with time-dependent transition time. Comput Oper Res 86:41–53

  15. 15.

    Cui KK, Xiang JH, Zhang YL (2018) Mission planning optimization of video satellite for ground multi-object staring imaging. Adv Space Res 61(6):1476–1489

  16. 16.

    Xu R, Chen H, Liang X (2016) Priority-based constructive algorithms for scheduling agile earth observation satellites with total priority maximization. Expert Syst Appl 51:195–206

  17. 17.

    Zhu KJ, Li JF, Baoyin HX (2010) Satellite scheduling considering maximum observation coverage time and minimum orbital transfer fuel cost. Acta Astronaut 66(1–2):220–229

  18. 18.

    Qiu DS, Guo H, He C, Wu GH (2013) Intensive task scheduling method for multi-agile imaging satellites. Acta Aeronauti et Astronauti Sinica 34(4):882–889

  19. 19.

    Xhafa F, Sun J, Barolli A, Biberaj A, Barolli L (2012) Genetic algorithms for satellite scheduling problems. Mob Inf Syst 8(4):351–377

  20. 20.

    Robinson E, Balakrishnan H, Abramson M, Kolitz S (2017) Optimized stochastic coordinated planning of asynchronous air and space assets. J Aerosp Inf Syst 14(1):10–25

  21. 21.

    Nag S, Li AS, Merrick JH (2018) Scheduling algorithms for rapid imaging using agile Cubesat constellations. Adv Space Res 61(3):891–913

  22. 22.

    Karapetyan D, Minic SM, Malladi KT, Punnen AP (2015) Satellite downlink scheduling problem: a case study. Omega 53:115–123

  23. 23.

    Barbulescu L, Watson JP, Whitley LD, Howe AE (2004) Scheduling space-ground communications for the air force satellite control network. J Sched 7(1):7–34

  24. 24.

    Marinelli F, Nocella S, Rossi F, Smriglio S (2011) A Lagrangian heuristic for satellite range scheduling with resource constraints. Comput Oper Res 38(11):1572–1583

  25. 25.

    Zhang Z, Zhang N, Feng Z (2014) Multi-satellite control resource scheduling based on ant colony optimization. Expert Syst Appl 4(6):2816–2823

  26. 26.

    Spangeloa S, Cutlera J, Gilsonb K, Cohn A (2015) Optimization-based scheduling for the single-satellite, multi-ground station communication problem. Comput Oper Res 57:1–16

  27. 27.

    Li YF, Wu XY (2008) Model of satellite data transmission scheduling problem based on multi-satellite combined reconnaissance. J B Univ Aeronaut Astronaut 34(8):948–960

  28. 28.

    Chen H, Li LM, Zhong ZN, Li J (2015) Approach for earth observation satellite real-time and playback data transmission scheduling. J Syst Eng Electron 26(5):982–992

  29. 29.

    Wang J, Jing N, Li J, Chen ZH (2007) A multi-objective imaging scheduling approach for earth observing satellites. In: Proceedings of the 9th annual conference on genetic and evolutionary computation, London, England, pp 2211–2218

  30. 30.

    Wang P, Reinelt G, Gao P, Tan YJ (2011) A model, a heuristic and a decision support system to solve the scheduling problem of an earth observing satellite constellation. Comput Ind Eng 61(2):322–335

  31. 31.

    Zhu WM, Hu XX, Xia W, Jin P (2019) A two-phase genetic annealing method for integrated Earth observation satellite scheduling problems. Soft Comput 23(1):181–196

  32. 32.

    Sarkheyli A, Bagheri A, Ghorbani-Vaghei B, Askari-Moghadam R (2013) Using an effective tabu search in interactive resources scheduling problem for LEO satellites missions. Aerosp Sci Technol 29(1):287–295

  33. 33.

    Li ZL, Li XJ, Sun W (2017) Task scheduling model and algorithm for AEOS considering imaging quality. J Astronaut 38(6):579–590

  34. 34.

    Chen XY, Reinelt G, Dai GM, Wang MC (2018) Priority-based and conflict-avoidance heuristics for multi-satellite scheduling. Appl Soft Comput 69:177–191

  35. 35.

    Gong WY, Cai Z, Liang D (2015) Adaptive ranking mutation operator based differential evolution for constrained optimization. IEEE Trans Cybern 45(4):716–727

  36. 36.

    Hashemi A, Hoseinpour-Gollo M, Seyedkashi SMH, Pourkamali-Anaraki A (2017) A new simulation-based meta-heuristic approach in optimization of bilayer composite sheet hydroforming. J Braz Soc Mech Sci Eng 39(10):4011–4020

  37. 37.

    Li JQ, Pan QK, Liang YC (2010) An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 59(4):647–662

  38. 38.

    Zhou Y, Wang JH, Wu ZY, Wu KK (2018) A multi-objective tabu search algorithm based on decomposition for multi-objective unconstrained binary quadratic programming problem. Knowl Based Syst 141:18–30

  39. 39.

    Hynes NRJ, Kumar R (2017) Process optimization for maximizing bushing length in thermal drilling using integrated ANN-SA approach. J Braz Soc Mech Sci Eng 39(12):5097–5108

  40. 40.

    Wang JL, Jagannathan AKR, Zuo XQ, Murray CC (2017) Two-layer simulated annealing and tabu search heuristics for a vehicle routing problem with cross docks and split deliveries. Comput Ind Eng 112:84–98

  41. 41.

    Rai P, Barman AG (2018) Design optimization of spur gear using SA and RCGA. J Braz Soc Mech Sci Eng 40:257. https://doi.org/10.1007/s40430-018-1180-y

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61773120, 61873328), the National Natural Science Fund for Distinguished Young Scholars of China (61525304), the Foundation for the Author of National Excellent Doctoral Dissertation of China (2014-92) and the Hunan Postgraduate Research Innovation Project (CX2018B022).

Author information

Correspondence to Lining Xing or Teng Ren.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest in this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Technical Editor: André Cavalieri.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Du, Y., Xing, L., Chen, Y. et al. Integrated agile observation satellite scheduling problem considering different memory environments: a case study. J Braz. Soc. Mech. Sci. Eng. 42, 76 (2020). https://doi.org/10.1007/s40430-019-2121-0

Download citation

Keywords

  • Agile earth observation satellite
  • Satellite scheduling
  • Memory environment
  • Integrated model
  • Meta-heuristics