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Soft Computing
Erschienen in: Soft Computing 1/2019

25.10.2017 | Methodologies and Application

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

verfasst von: Zhu Waiming, Hu Xiaoxuan, Xia Wei, Jin Peng

Erschienen in: Soft Computing | Ausgabe 1/2019

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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.
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Metadaten
Titel
A two-phase genetic annealing method for integrated Earth observation satellite scheduling problems
verfasst von
Zhu Waiming
Hu Xiaoxuan
Xia Wei
Jin Peng
Publikationsdatum
25.10.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 1/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-017-2889-8

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