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

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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.

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Agile earth observation satellite


Holistically erasable memory


Partially erasable memory


Simulated annealing algorithm


Hybrid Tabu-simulated annealing algorithm


Observation time window


Transmission time window


Tabu search algorithm


Late acceptance algorithm


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


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 :


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


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


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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).

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Correspondence to Lining Xing or Teng Ren.

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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).

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