Test Schedule Generation for Acceptance Testing of Mission-Critical Satellite Systems

2025-01-02

AI Summary

Satellite in-orbit testing (IOT) scheduling faces multiple constraints—including visibility windows, ground station operational costs, time-window restrictions, and resource conflicts—making manual or heuristic scheduling inefficient and suboptimal. Method: This paper formulates IOT scheduling as a multi-objective optimization problem for the first time, jointly minimizing operational cost, fragmentation (i.e., maximizing test continuity), and maximizing ground station resource utilization. We propose an NSGA-II–based optimization framework incorporating domain-specific constraint modeling and a problem-aware heuristic initialization strategy. Contribution/Results: Experiments demonstrate that our approach reduces operational cost by 49.4% and fragmentation by 60.4%, while improving resource utilization by 30%, compared to baseline methods. Against manual scheduling, it achieves a 538% improvement in cost efficiency, a 39.42% gain in resource efficiency, and completes scheduling in only 12.5% of the time. The method significantly enhances automation and practical deployability of IOT scheduling in operational satellite programs.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Satisfiability
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Large-scale security measurements

Abstract

Mission-critical system, such as satellite systems, healthcare systems, and nuclear power plant control systems, undergo rigorous testing to ensure they meet specific operational requirements throughout their operation. This includes Operational Acceptance Testing (OAT), which aims to ensure that the system functions correctly under real-world operational conditions. In satellite development, In-Orbit Testing (IOT) is a crucial OAT activity performed regularly and as needed after deployment in orbit to check the satellite's performance and ensure that operational requirements are met. The scheduling of an IOT campaign, which executes multiple IOT procedures, is an important yet challenging problem, as it accounts for various factors, including satellite visibility, antenna usage costs, testing time periods, and operational constraints. To address the IOT scheduling problem, we propose a multi-objective approach to generate near-optimal IOT schedules, accounting for operational costs, fragmentation (i.e., the splitting of tests), and resource efficiency, which align with practitioners' objectives for IOT scheduling. Our industrial case study with SES Techcom shows significant improvements, as follows: an average improvement of 49.4% in the cost objective, 60.4% in the fragmentation objective, and 30% in the resource usage objective, compared to our baselines. Additionally, our approach improves cost efficiency by 538% and resource usage efficiency by 39.42% compared to manually constructed schedules provided by practitioners, while requiring only 12.5% of the time needed for manual IOT scheduling.

Problem

• Satellite Systems
• Operational Acceptance Testing
• In-Orbit Testing

Innovation

• Optimized Test Scheduling
• Cost Efficiency
• Resource Utilization