Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty
This addresses a domain-specific problem for satellite mission planners, but appears incremental as it builds on existing methods like simulated annealing.
The paper tackles the problem of scheduling multiple agile Earth observation satellites under cloud coverage uncertainty to maximize observation profit, and proposes an improved simulated annealing heuristic that outperforms other algorithms.
Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments equipped on satellites cannot see through the cloud, the cloud coverage has a significant influence on the satellite observation missions. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. The chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via sample approximation method. Subsequently, an improved simulated annealing based heuristic combining a fast insertion strategy is proposed for large-scale observation missions. The experimental results show that the improved simulated annealing heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which verifies the efficiency and effectiveness of the proposed algorithm.