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A unified scheduling approach for earth observation satellites with large-scale and heterogeneous tasks.

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Title: A unified scheduling approach for earth observation satellites with large-scale and heterogeneous tasks.
Authors: Xing, Ligang1,2 (AUTHOR) xingligang@mail.hfut.edu.cn, Hu, Xiaoxuan1,2,3 (AUTHOR) xiaoxuanhu@hfut.edu.cn, Hu, Nan4 (AUTHOR) anhu@smu.edu.sg, Xia, Wei1,2 (AUTHOR) xiawei@hfut.edu.cn, Sun, Haiquan1,3 (AUTHOR) sunhaiquan@hfut.edu.cn
Source: Expert Systems with Applications. Apr2026, Vol. 304, pN.PAG-N.PAG. 1p.
Subjects: Scheduling, Artificial satellites, Earth Observing System (Program), Simulation methods & models, Combinatorial optimization, Global optimization
Abstract: • Unified approach for scheduling Earth observation satellites with diverse tasks. • Three-stage transformation reformulates scheduling as strip selection problem. • Two-layer framework separates feasibility construction and global optimization. • Scalable, robust approach delivers high-quality schedules across diverse instances. As Earth observation tasks continue to grow in scale and exhibit increasingly diverse requirements, scheduling for Earth observation satellites (EOSs) faces significant new challenges. These include the need to integrate heterogeneous tasks–each with distinct spatial, temporal, and resource constraints–into a unified scheduling approach, and to efficiently solve the resulting large-scale combinatorial optimization problems. To address these challenges, we first reformulate the EOS scheduling problem with large-scale and heterogeneous tasks into a strip selection problem based on imaging opportunities through a three-stage transformation. This transformation involves grid-based discretization of area targets, construction of imaging opportunities (IOs, i.e., time intervals within which an EOS may perform an imaging action), and generation of candidate strips (i.e., specific imaging actions associated with each IO, each indicating how the EOS observes a specific region on the ground). We then propose a two-layer scheduling framework that separates the construction of feasible schedules from global optimization to solve the reformulated problem. The first layer applies our proposed dynamic strip selection (DSS) algorithm to construct a feasible schedule by sequentially selecting the strip with the highest marginal benefit for each IO. To improve global coordination, the Sequence Optimization Layer applies a Variable Neighborhood Search (VNS) algorithm to explore alternative IO sequences, leveraging the first layer to construct and evaluate candidate schedules. Computational experiments demonstrate that the DSS, when applied to a time-based IO sequence, generates initial solutions with an average gap of 7.8 % relative to a theoretical upper bound within 0.4 s. This gap is further reduced to 3.1 % through VNS-based sequence optimization. Additional comparisons and evaluations on extremely large instances confirm the effectiveness, stability, and scalability of the proposed approach. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• Unified approach for scheduling Earth observation satellites with diverse tasks. • Three-stage transformation reformulates scheduling as strip selection problem. • Two-layer framework separates feasibility construction and global optimization. • Scalable, robust approach delivers high-quality schedules across diverse instances. As Earth observation tasks continue to grow in scale and exhibit increasingly diverse requirements, scheduling for Earth observation satellites (EOSs) faces significant new challenges. These include the need to integrate heterogeneous tasks–each with distinct spatial, temporal, and resource constraints–into a unified scheduling approach, and to efficiently solve the resulting large-scale combinatorial optimization problems. To address these challenges, we first reformulate the EOS scheduling problem with large-scale and heterogeneous tasks into a strip selection problem based on imaging opportunities through a three-stage transformation. This transformation involves grid-based discretization of area targets, construction of imaging opportunities (IOs, i.e., time intervals within which an EOS may perform an imaging action), and generation of candidate strips (i.e., specific imaging actions associated with each IO, each indicating how the EOS observes a specific region on the ground). We then propose a two-layer scheduling framework that separates the construction of feasible schedules from global optimization to solve the reformulated problem. The first layer applies our proposed dynamic strip selection (DSS) algorithm to construct a feasible schedule by sequentially selecting the strip with the highest marginal benefit for each IO. To improve global coordination, the Sequence Optimization Layer applies a Variable Neighborhood Search (VNS) algorithm to explore alternative IO sequences, leveraging the first layer to construct and evaluate candidate schedules. Computational experiments demonstrate that the DSS, when applied to a time-based IO sequence, generates initial solutions with an average gap of 7.8 % relative to a theoretical upper bound within 0.4 s. This gap is further reduced to 3.1 % through VNS-based sequence optimization. Additional comparisons and evaluations on extremely large instances confirm the effectiveness, stability, and scalability of the proposed approach. [ABSTRACT FROM AUTHOR]
ISSN:09574174
DOI:10.1016/j.eswa.2025.130724