# A Mixed Integer Linear Programming Model for Multi-Satellite Scheduling

arXiv: https://arxiv.org/abs/1811.12114 · HTML: https://mlsanity.com/paper/1811.12114/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem](https://mlsanity.com/paper/2206.05694/) (2022)
- [A Constraint Programming Model for the Super-Agile Earth Observation Satellite Imaging Scheduling Problem](https://mlsanity.com/paper/2601.11967/) (2026)
- [Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem](https://mlsanity.com/paper/2301.02764/) (2023)
- [Monte Carlo Tree Search Satellite Scheduling Under Cloud Cover Uncertainty](https://mlsanity.com/paper/2405.20951/) (2024)
- [Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study](https://mlsanity.com/paper/2506.15207/) (2025)
- [HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster](https://mlsanity.com/paper/2605.31023/) (2026)

## Abstract (arXiv)

We address the multi-satellite scheduling problem with limited observation capacities that arises from the need to observe a set of targets on the Earth's surface using imaging resources installed on a set of satellites. We define and analyze the conflict indicators of all available visible time windows of missions, as well as the feasible time intervals of resources. The problem is then formulated as a mixed integer linear programming model, in which constraints are derived from a careful analysis of the interdependency between feasible time intervals that are eligible for observations. We apply the proposed model to several different problem instances that reflect real-world situations. The computational results verify that our approach is effective for obtaining optimum solutions or solutions with a very good quality.