A three-phase solution method for the scheduling problem of using earth observation satellites to observe polygon requests

Zhu, Waiming; Hu, Xiaoxuan; Wei, Xiaoying; Sun, Haoyu

doi:10.1016/j.cie.2019.02.014

Cited by 18 publications

(11 citation statements)

References 21 publications

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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: 94%

“…In that case, the gurobi solver (a widely used solver, we have obtained a license) is used to solve the optimal resource allocation route, and then the current unallocated resources are updated (lines 11-13). If no new resource requests are added, the optimal resource allocation plan is resolved using gurobi, then the currently unallocated resources are updated and returned to the current minimum time resource allocation plan (lines [18][19][20].…”

Section: Resources Type Sources Numbermentioning

confidence: 99%

See 1 more Smart Citation

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: 94%

Section: Resources Type Sources Numbermentioning

confidence: 99%

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Fan

Yang

et al. 2021

Mathematics

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“…Single EOS Exact [6], [7], Heuristic [8], [9] − Multiple EOSs MOEA [10], [11], Heuristic [12], [13], [14], [15] MOEA [16], [17], Heuristic [18], [19], [20], [21], [22], [23] Large A considerable amount of literature has been published on the point targets observation scheduling problem over the past decades. Existing algorithms to solve the Earth observation scheduling problem can be divided into three types: the exact algorithm [24], [25], [26], heuristic [27], [28], [29], [30], and multi-objective evolutionary algorithm (MOEA) [31].…”

Section: Single Region Multiple Regionsmentioning

confidence: 99%

“…Wang et al [18] established a nonlinear model and presented a heuristic for solving the disaster monitoring observation scheduling problem by four satellites. For the polygon region observation request, Zhu et al [19] proposed a three-phase solution method that integrates with a dynamic greedy algorithm and a tabu search algorithm. Moreover, the scheduling problem for the synthetic aperture radar satellite, which owns a specific field of view, has been researched for area observing [20], [21].…”

Section: Single Region Multiple Regionsmentioning

confidence: 99%

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Gu¹,

Chen²,

Chen³

et al. 2022

Preprint

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The last decades have witnessed a rapid increase of Earth observation satellites (EOSs), leading to the increasing complexity of EOSs scheduling. On account of the widespread applications of large region observation, this paper aims to address the EOSs observation scheduling problem for large region targets. A rapid coverage calculation method employing a projection reference plane and a polygon clipping technique is first developed. We then formulate a nonlinear integer programming model for the scheduling problem, where the objective function is calculated based on the developed coverage calculation method. A greedy initialization-based resampling particle swarm optimization (GI-RPSO) algorithm is proposed to solve the model. The adopted greedy initialization strategy and particle resampling method contribute to generating efficient and effective solutions during the evolution process. In the end, extensive experiments are conducted to illustrate the effectiveness and reliability of the proposed method. Compared to the traditional particle swarm optimization and the widely used greedy algorithm, the proposed GI-RPSO can improve the scheduling result by 5.42% and 15.86%, respectively.

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“…Due to the linear assumption of tasks transition time, the proposed integrated AEOSSP model actually degrades to the CEOSSP and is therefore can be easily solved by a commercial solver. Meanwhile, several recent research in integrated CEOSSP [96,100] has been conducted. However, the integrated AEOSSP still needs to be further investigated.…”

Section: Integrated Schedulingmentioning

confidence: 99%

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

Wang,

Wu,

Xing

et al. 2020

Preprint

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Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this paper are thus to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multi-objective function and autonomous model. A detailed literature review from 1997 up to 2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic and machine learning. Finally, we discuss a number of topics worth pursuing in the future.

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A three-phase solution method for the scheduling problem of using earth observation satellites to observe polygon requests

Cited by 18 publications

References 21 publications

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

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