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Learning to Construct a Solution for the Agile Satellite Scheduling Problem With Time-Dependent Transition Times | IEEE Journals & Magazine | IEEE Xplore

Learning to Construct a Solution for the Agile Satellite Scheduling Problem With Time-Dependent Transition Times


Abstract:

The agile earth observation satellite scheduling problem (AEOSSP) with time-dependent transition times is a complex combinational optimization problem that has emerged fr...Show More

Abstract:

The agile earth observation satellite scheduling problem (AEOSSP) with time-dependent transition times is a complex combinational optimization problem that has emerged from the development of large-scale satellite management techniques. To address this problem, we propose a deep reinforcement learning-based construction model (DRL-CM) that consists of five parts: 1) a Markov decision process (MDP); 2) a feature engineering; 3) a constructive heuristic neural network (CHNN); 4) an RL training method; and 5) an evaluation system. Specifically, the CHNN comprises six modules containing three special components that we propose: a dynamic encoder, a dynamic global layer, and a two-stage attention layer. First, we build the MDP of the AEOSSP and the feature engineering with effective features required for decision-making. Second, we design the CHNN to function as the MDP policy and train it with an RL model. Finally, we propose a comprehensive evaluation system for the validation of our model. The experimental results indicate that the proposed DRL-CM outperforms the state-of-the-art algorithm in terms of both optimization speed and quality. In addition, the feature engineering and network architecture built in our model are verified to be effective in comprehensive experiments.
Page(s): 5949 - 5963
Date of Publication: 09 July 2024

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