Multi-Objective Optimization Modeling and Solution of Multi-Satellite TT&amp;lC Scheduling Problem

Qi, Weihua; Du, Yonghao; Xing, Lining; Chen, Yuning

doi:10.1109/ssci44817.2019.9002998

Cited by 2 publications

(1 citation statement)

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“…It proposes a finite embedded infinite two-layer dynamic programming framework, transforming the scheduling optimization problem into a discrete Markov decision process (MDP). Papers [20][21][22][23] considered the complex constraints faced in the process of satellite resource scheduling, combined with computational geometry and other theories for modeling, and verified the effectiveness of the model based on data from many instances. Ref.…”

Section: Resource Scheduling Methods Based On Machine Learningmentioning

confidence: 93%

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Fan

Yang

et al. 2021

Mathematics

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To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during inter-satellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.

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Section: Resource Scheduling Methods Based On Machine Learningmentioning

confidence: 93%