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Efficient scheduling is of great significance to rationally make use ofscarce satellite resources. Task clustering has been demonstrated to realize aneffective strategy to improve the efficiency of satellite scheduling. However,the previous task clustering strategy is static. That is, it is integrated intothe scheduling in a two-phase manner rather than in a dynamic fashion, withoutexpressing its full potential in improving the satellite schedulingperformance. In this study, we present an adaptive Simulated Annealing basedscheduling algorithm aggregated with a dynamic task clustering strategy (orASA-DTC for short) for satellite observation scheduling problems (SOSPs).First, we develop a formal model for the scheduling of Earth observingsatellites. Second, we analyze the related constraints involved in theobservation task clustering process. Thirdly, we detail an implementation ofthe dynamic task clustering strategy and the adaptive Simulated Annealingalgorithm. The adaptive Simulated Annealing algorithm is efficient, with theendowment of some sophisticated mechanisms, i.e. adaptive temperature control,tabu-list based revisiting avoidance mechanism, and intelligent combination ofneighborhood structures. Finally, we report on experimental simulation studiesto demonstrate the competitive performance of ASA-DTC. Moreover, we show thatASA-DTC is especially effective when SOSPs contain a large number of targets orthese targets are densely distributed in a certain area.

An adaptive Simulated Annealing-based satellite observation scheduling method combined with a dynamic task clustering strategy