# Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty

arXiv: https://arxiv.org/abs/2003.08363 · HTML: https://mlsanity.com/paper/2003.08363/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization](https://mlsanity.com/paper/2206.10178/) (2022)
- [An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling](https://mlsanity.com/paper/2602.15070/) (2026)
- [An Automated Tip-and-Cue Framework for Optimized Satellite Tasking and Visual Intelligence](https://mlsanity.com/paper/2512.09670/) (2025)
- [Efficient Policy Learning with Hybrid Evaluation-Based Genetic Programming for Uncertain Agile Earth Observation Satellite Scheduling](https://mlsanity.com/paper/2603.08447/) (2026)
- [Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach](https://mlsanity.com/paper/2604.13283/) (2026)
- [A general optimization solver based on OP-to-MaxSAT reduction](https://mlsanity.com/paper/2604.21961/) (2026)
- [Task-Driven Three-Layer Distributed Scheduling for Emergency Earth Observation in Large Low-Earth-Orbit Constellations](https://mlsanity.com/paper/2608.14789/) (2026)
- [Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling](https://mlsanity.com/paper/2608.24470/) (2026)

## Abstract (arXiv)

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.