Robust scheduling for multiple agile Earth observation satellites under cloud coverage uncertainty

Abstract Agile satellites are the next generation of Earth observation satellites (EOSs) because they can rotate on three axes while EOSs cannot. Due to this improvement, agile EOS scheduling has attracted much attention. Meanwhile, the influence of cloud coverage for EOS is inevitable for most satellites equipped with optical remote sensing instruments, causing a significant impact on the quality of the images obtained, which therefore impacts on practical satellite scheduling. To deal with the agile satellite scheduling problem under cloud coverage uncertainty, we introduce the concept of budgeted uncertainty set and establish a linearized robust scheduling model, where the budget value should be determined by the satellite operation cost and the conservative degree. Then the original problem is transformed into a master problem and multiple pricing problems based on the column generation. Considering the enormous number of variables introduced by the dual transformation in the robust model, we propose a hybrid heuristic including column generation and simulated annealing. Finally, extensive simulation experiments are conducted to verify the effectiveness and efficiency of the proposed method.