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Soft Computing

, Volume 23, Issue 1, pp 181–196 | Cite as

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

  • Zhu Waiming
  • Hu XiaoxuanEmail author
  • Xia Wei
  • Jin Peng
Methodologies and Application

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.

Keywords

EOS Integrated scheduling Two-phase solution method Genetic annealing method Automatic switching 

Notes

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.

Compliance with ethical standards

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.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Zhu Waiming
    • 1
    • 2
  • Hu Xiaoxuan
    • 1
    • 2
    Email author
  • Xia Wei
    • 1
    • 2
  • Jin Peng
    • 1
    • 2
  1. 1.School of ManagementHefei University of TechnologyHefeiChina
  2. 2.Key Laboratory of Process Optimization and Intelligent Decision-makingMinistry of EducationHefeiChina

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