LLM Based Bi-Level Online Order Dispatching and Scheduling for Large-Scale Earth Observation Satellites

With the increasing demand for scheduling large-scale Earth Observation Satellites (EOS), traditional scheduling models are no longer sufficient to meet the requirements for flexibility and order-driven operations. To address this challenge, this paper proposes a Large Language Model (LLM)-based Bi-level Online Order Dispatching and Scheduling (LLM-BODS) algorithm. The proposed algorithm consists of three key components: a bi-level online optimization framework, an LLM-based order dispatching algorithm, and a metaheuristic algorithm for single-satellite order scheduling. Within the bi-level optimization framework, the upper level dynamically allocates orders to satellites based on real-time order demands and satellite resource states using the LLM-based order dispatching algorithm. The lower level employs an adaptive large neighborhood search (ALNS) metaheuristic to generate optimal task execution sequences for each satellite. Experimental results demonstrate that the proposed algorithm not only maintains scheduling quality but also enhances scheduling efficiency, effectively addressing the flexible scheduling needs of large-scale Earth Observation Satellites.