An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling

2026-02-14

AI Summary

This study addresses the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP), where critical information such as task rewards, resource consumption, and visibility windows is unknown a priori. For the first time, genetic programming (GP) is employed as a hyper-heuristic framework to automatically evolve online-adaptable scheduling policies. By integrating uncertainty modeling with real-time decision-making, the proposed approach significantly outperforms both manually designed and look-ahead heuristic methods. Experimental results demonstrate that the evolved policies achieve an average performance improvement of 5.03% over look-ahead heuristics and 8.14% over handcrafted heuristics, exhibiting superior adaptability and robustness in dynamic and uncertain environments.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Heuristic Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper investigates a novel problem, namely the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP). Unlike the static AEOSSP, it takes into account a range of uncertain factors (e.g., task profit, resource consumption, and task visibility) in order to reflect the reality that the actual information is inherently unknown beforehand. An effective Genetic Programming Hyper-Heuristic (GPHH) is designed to automate the generation of scheduling policies. The evolved scheduling policies can be utilized to adjust plans in real time and perform exceptionally well. Experimental results demonstrate that evolved scheduling policies significantly outperform both well-designed Look-Ahead Heuristics (LAHs) and Manually Designed Heuristics (MDHs). Specifically, the policies generated by GPHH achieve an average improvement of 5.03% compared to LAHs and 8.14% compared to MDHs.

Problem

• Uncertain Agile Earth Observation Satellite Scheduling
• satellite scheduling
• uncertainty
• task visibility
• resource consumption

Innovation

• Genetic Programming Hyper-Heuristic
• Uncertain Satellite Scheduling
• Agile Earth Observation
• Automated Policy Generation
• Real-time Scheduling