Skip to main content

Advertisement

SpringerLink
Log in

A two-phase genetic annealing method for integrated Earth observation satellite scheduling problems

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper investigates an integrated approach to Earth observation satellite scheduling (EOSS) and proposes a two-phase genetic annealing (TPGA) method to solve the scheduling problem. Standard EOSS requires the development of feasible imaging schedules for Earth observation satellites. However, integrated EOSS is more complicated, mainly because both imaging and data transmission operations are of equal concern. In this paper, we first establish a mixed integer linear programming model for the scheduling problem using a directed acyclic graph for determining candidate solution options. Then, we optimize the model by applying the TPGA method, which consists of two phases in which a genetic algorithm is first employed, followed by simulated annealing. Detailed designs of the algorithm integration and algorithm switching rules are provided based on reasonable deductions. Finally, simulation experiments are conducted to demonstrate the feasibility and optimality of the proposed TPGA method.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abdelbar AM,&Hosny MI (2006) Finding most probable explanations using a self-adaptive hybridization of genetic algorithms and simulated annealing. In: Proceedings of the 10th WSEAS international conference on Computers. World Scientific and Engineering Academy and Society (WSEAS), pp 810–816

  • Adler D (1993) Genetic algorithms and simulated annealing: a marriage proposal. In: Neural Networks, 1993., IEEE International Conference on, IEEE, pp 1104–1109

  • Bensana E, Lemaitre M, Verfaillie G (1999) Earth observation satellite management. Constraints 4(3):293–299

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Bianchessi N, Cordeau JF, Desrosiers 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

    Article  MATH  Google Scholar 

  • Bouleimen KLEIN, Lecocq HOUSNI (2003) A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. Eur J Oper Res 149(2):268–281

    Article  MathSciNet  MATH  Google Scholar 

  • Brown DE, Huntley CL,&Spillane AR (1989) A parallel genetic heuristic for the quadratic assignment problem. In: Proceedings of the 3rd international conference on genetic algorithms, Morgan Kaufmann Publishers Inc, pp 406–415

  • Chen SM, Chien CY (2011) Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques. Expert Syst Appl 38(12):14439–14450

    Article  Google Scholar 

  • Chen PH, Shahandashti SM (2009) Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Autom Constr 18(4):434–443

    Article  Google Scholar 

  • Chen Y, Zhang D, Zhou M, Zou H (2012) Multi-satellite observation scheduling algorithm based on hybrid genetic particle swarm optimization. In: Zeng D (ed) Advances in information technology and industry applications. Springer, Berlin, Heidelberg, pp 441–448

    Chapter  Google Scholar 

  • Gabrel V (2006) Strengthened 0–1 linear formulation for the daily satellite mission planning. J Comb Optim 11(3):341–346

    Article  MathSciNet  MATH  Google Scholar 

  • Gabrel V, Vanderpooten D (2002) Enumeration and interactive selection of efficient paths in a multiple criteria graph for scheduling an earth observing satellite. Eur J Oper Res 139(3):533–542

    Article  MATH  Google Scholar 

  • Ganesh K, Punniyamoorthy M (2005) Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing. Int J Adv Manuf Technol 26(1):148–154

    Article  Google Scholar 

  • Grefenstette JJ (1987) Incorporating problem-specific knowledge in genetic algorithms. In: Davis (ed) Genetic algorithms and simulated annealing. Pitman, London, pp 42–60

  • Globus A, Crawford J, Lohn J, Pryor A (2004) A comparison of techniques for scheduling earth observing satellites. In: AAAI, pp 836–843

  • Gu H (2016) Improving problem reduction for 0–1 multidimensional Knapsack problems with valid inequalities. Comput Oper Res 71:82–89

    Article  MathSciNet  MATH  Google Scholar 

  • Habet D, Vasquez M, Vimont Y (2010) Bounding the optimum for the problem of scheduling the photographs of an Agile Earth observing satellite. Comput Optim Appl 47(2):307–333

    Article  MathSciNet  MATH  Google Scholar 

  • Haynes W (2013) Student’s t-test. In: HaynesW (ed) Encyclopedia of systems biology. Springer, New York, pp 2023–2025

  • Hui S (2010) Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulated annealing genetic algorithm. Eng Appl Artif Intell 23(1):27–33

    Article  Google Scholar 

  • Huntley CL, Brown DE (1991) A parallel heuristic for quadratic assignment problems. Comput Oper Res 18(3):275–289

    Article  MATH  Google Scholar 

  • Kulkarni AJ, Shabir H (2016) Solving 0–1 knapsack problem using cohort intelligence algorithm. Int J Mach Learn Cybern 7(3):427–441

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Leung TW, Chan CK, Troutt MD (2003) Application of a mixed simulated annealing-genetic algorithm heuristic for the two-dimensional orthogonal packing problem. Eur J Oper Res 145(3):530–542

    Article  MathSciNet  MATH  Google Scholar 

  • Li XG, Wei X (2008) An improved genetic algorithm-simulated annealing hybrid algorithm for the optimization of multiple reservoirs. Water Resour Manag 22(8):1031–1049

    Article  Google Scholar 

  • Li WD, Ong SK, Nee AYC (2002) Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int J Prod Res 40(8):1899–1922

    Article  MATH  Google Scholar 

  • Lilliefors HW (1967) On the Kolmogorov–Smirnov test for normality with mean and variance unknown. J Am Stat Assoc 62(318):399–402

    Article  Google Scholar 

  • Liu X, Bai B, Yingwu C, Feng Y (2014) Multi satellites scheduling algorithm based on task merging mechanism. Appl Math Comput 230:687–700

    MathSciNet  MATH  Google Scholar 

  • Miu KN, Chiang HD, Darling G (1997) Capacitor placement, replacement and control in large-scale distribution systems by a GA-based two-stage algorithm. IEEE Trans Power Syst 12(3):1160–1166

    Article  Google Scholar 

  • O’Mahony M (1986) Sensory evaluation of food: statistical methods and procedures. CRC Press, Boca Raton

    Google Scholar 

  • Pelton JN, Madry S, Camacho-Lara S (2012) Handbook of satellite applications [M]. Springer Publishing Company, Incorporated, Berlin

    Google Scholar 

  • Peng G, Wen L, Feng Y, Baocun B, Jing Y (2011) Simulated annealing algorithm for EOS scheduling problem with task merging. In: Modelling, identification and control (ICMIC), proceedings of 2011 international conference on, IEEE, pp 547–552

  • Ponnambalam SG, Reddy M (2003) A GA-SA multiobjective hybrid search algorithm for integrating lot sizing and sequencing in flow-line scheduling. Int J Adv Manuf Technol 21(2):126–137

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Spangelo S, Cutler J, Gilson K et al (2015) Optimization-based scheduling for the single-satellite, multi-ground station communication problem[J]. Comput Oper Res 57:1–16

    Article  MathSciNet  MATH  Google Scholar 

  • Tangpattanakul P, Jozefowiez N, Lopez P (2015a) Biased random key genetic algorithm for multi-user Earth observation scheduling. In: Fidanova S (ed) Recent advances in computational optimization, Springer International Publishing, pp 143–160

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

    Article  MathSciNet  MATH  Google Scholar 

  • Vasquez M, Hao JK (2001) A logic-constrained knapsack formulation and a tabu algorithm for the daily photograph scheduling of an earth observation satellite. Comput Optim Appl 20(2):137–157

    Article  MathSciNet  MATH  Google Scholar 

  • Verfaillie G, Lemaître M, Schiex T(1996) Russian doll search for solving constraint optimization problems. In: AAAI/IAAI, vol. 1, pp 181–187

  • Wang P, Reinelt G, Gao P, Tan Y (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

    Article  Google Scholar 

  • Wang J, Zhu X, Yang LT, Zhu J, Ma M (2015) Towards dynamic real-time scheduling for multiple earth observation satellites. J Comput Syst Sci 81(1):110–124

    Article  Google Scholar 

  • Wu G, Liu J, Ma M, Qiu D (2013) A two-phase scheduling method with the consideration of task clustering for earth observing satellites. Comput Oper Res 40(7):1884–1894

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Yenisey MM, Yagmahan B (2014) Multi-objective permutation flow shop scheduling problem: literature review, classification and current trends. Omega 45:119–135

    Article  Google Scholar 

  • Zhang C, Li P, Rao Y, Li S (2005) A new hybrid GA/SA algorithm for the job shop scheduling problem. In: Raidl GR, Gottlieb J (eds) Evolutionary computation in combinatorial optimization. EvoCOP 2005. Lecture notes in computer science, vol 3448. Springer, Berlin, Heidelberg, pp 246–259

  • Zhang D, Guo L, Cai B, Sun N, Wang Q (2013) A hybrid discrete particle swarm optimization for satellite scheduling problem. In: Conference anthology, IEEE, IEEE, pp 1-5

  • Zhang Z, Zhang N, Feng Z (2014) Multi-satellite control resource scheduling based on ant colony optimization. Expert Syst Appl 41(6):2816–2823

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the editors and two anonymous referees for their constructive suggestions which have enabled the authors to significantly improve the paper. This study was funded by the National Natural Science Foundation of China under Grants 71521001, 71671059 and 71401048.

Author information

Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, China

    Zhu Waiming, Hu Xiaoxuan, Xia Wei & Jin Peng

  2. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, China

    Zhu Waiming, Hu Xiaoxuan, Xia Wei & Jin Peng

Authors
  1. Zhu Waiming
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hu Xiaoxuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xia Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jin Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hu Xiaoxuan.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Human and animal rights

This article does not contain any studies with human participants performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Waiming, Z., Xiaoxuan, H., Wei, X. et al. A two-phase genetic annealing method for integrated Earth observation satellite scheduling problems. Soft Comput 23, 181–196 (2019). https://doi.org/10.1007/s00500-017-2889-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2889-8

Keywords

Search

Navigation