Scheduling Techniques in Astronomical Observation: Survey, Taxonomy, Challenges, and Future Trends

ABSTRACT Astronomical observation scheduling becomes a prominent issue due to the urgent need for astronomers to conduct a comprehensive exploration of the dynamic universe. In recent decades, an increase in survey area and spatio‐temporal monitoring frequency is raising the complexity of scheduling decisions. Inefficient scheduling technologies face issues such as inefficient and inflexible decision‐making, overutilized resources, and imbalanced resource allocation, which are not conducive to scientific output. Astronomical observation facilities are expensive and have a limited life cycle, so the improvement of observation efficiency is crucial. A variety of time‐varying constraints, such as scientific objectives, observation resource performance, and astronomical observation conditions, pose great challenges to this problem. Hence, proper strategies for task scheduling and telescope coordination are indispensable. A comprehensive taxonomic review of the current state‐of‐the‐art scheduling techniques is presented using exhaustive evaluation criteria. An extensive list of scheduling objectives, along with their associated evaluation metrics, has been considered in this study. A rigorous evaluation of existing literature based on key features is conducted, and their limitations are highlighted. Many factors and challenges in advancing astronomical observation scheduling for next‐generation automatic astronomical applications are also identified, and several potential recommendations are provided. This article is categorized under: Technologies > Artificial Intelligence Technologies > Machine Learning Application Areas > Science and Technology

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Publication Details

Journal
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
Published
2026-10-27
DOI
https://doi.org/10.1002/widm.70138
Primary Topic
Astronomy and Astrophysical Research
Type
article
Field-Weighted Citation Impact
0.00

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Scheduling Techniques in Astronomical Observation: Survey, Taxonomy, Challenges, and Future Trends

Zhaohui Shang, Yajie Zhang, Ce Yu, Chao Sun et al.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
Astronomy and Astrophysical Research
article

Scheduling Techniques in Astronomical Observation: Survey, Taxonomy, Challenges, and Future Trends

Zhaohui Shang, Yajie Zhang, Ce Yu, Chao Sun, Jizeng Wei, Yi Hu, Junhan Ju, Siyu Zhang
article en

Abstract

ABSTRACT Astronomical observation scheduling becomes a prominent issue due to the urgent need for astronomers to conduct a comprehensive exploration of the dynamic universe. In recent decades, an increase in survey area and spatio‐temporal monitoring frequency is raising the complexity of scheduling decisions. Inefficient scheduling technologies face issues such as inefficient and inflexible decision‐making, overutilized resources, and imbalanced resource allocation, which are not conducive to scientific output. Astronomical observation facilities are expensive and have a limited life cycle, so the improvement of observation efficiency is crucial. A variety of time‐varying constraints, such as scientific objectives, observation resource performance, and astronomical observation conditions, pose great challenges to this problem. Hence, proper strategies for task scheduling and telescope coordination are indispensable. A comprehensive taxonomic review of the current state‐of‐the‐art scheduling techniques is presented using exhaustive evaluation criteria. An extensive list of scheduling objectives, along with their associated evaluation metrics, has been considered in this study. A rigorous evaluation of existing literature based on key features is conducted, and their limitations are highlighted. Many factors and challenges in advancing astronomical observation scheduling for next‐generation automatic astronomical applications are also identified, and several potential recommendations are provided. This article is categorized under: Technologies > Artificial Intelligence Technologies > Machine Learning Application Areas > Science and Technology

Wiley Interdisciplinary Reviews Data Mining and Knowledge DiscoveryVol. 16(4)
Tianjin University (CN), Chinese Academy of Sciences (CN), National Astronomical Observatories (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 14%
Astronomy and Astrophysical Research
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