Journal of System Simulation
Abstract
Abstract: To further improve the execution efficiency of remote sensing satellites, an integrated optimization framework combining adaptive large neighborhood search (ALNS) and a constraint programming-boolean satisfiability problem (CP-SAT) solver monitor was proposed, addressing the challenges of complex constraints, dynamic scale, and resource heterogeneity in multi-scenario multi-satellite mission planning. A unified multi-objective mixed-integer programming model was established, coupling heterogeneous constraints of point targets and area tasks. A time-domain rolling mechanism dynamically decomposed the problem scale, and a priority screening strategy enhanced the search efficiency of ALNS. Solution feasibility was verified in real time through the CP-SAT monitor. Results show that compared with genetic algorithm, particle swarm optimization, and deep Q-network, the proposed method achieves a 15%~28% improvement in task completion rate, 30%~50% reduction in computation time, and over 20% optimization in load balancing in 300 test scenarios.
Recommended Citation
Li, Xiutian; Wang, Ling; Chen, Yingwu; Xing, Lining; and Chen, Yingguo
(2025)
"Multi-scenario Multi-satellite Mission Planning Method Based on Adaptive Large Neighborhood Search,"
Journal of System Simulation: Vol. 37:
Iss.
7, Article 15.
DOI: 10.16182/j.issn1004731x.joss.25-0095
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss7/15
First Page
1836
Last Page
1847
CLC
TP391.9
Recommended Citation
Li Xiutian, Wang Ling, Chen Yingwu, et al. Multi-scenario Multi-satellite Mission Planning Method Based on Adaptive Large Neighborhood Search[J]. Journal of System Simulation, 2025, 37(7): 1836-1847.
DOI
10.16182/j.issn1004731x.joss.25-0095
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