A Heuristic Approach to Satellite Range Scheduling With Bounds Using Lagrangian Relaxation

Brown, Nathanael J. K.; Arguello, Bryan; Nozick, Linda K.; Xu, Ningxiong

doi:10.1109/jsyst.2018.2821094

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…Noted that the a of tw k r,a is utilized to specify the exact antenna providing the time window resource, which is useful in conflict detecting procedure. The upper bound for each gene is the number of feasible time windows for request r. Equation (10) indicates each request corresponds no more than one time window, hence the execution and satellite uniqueness constraint (constraint (4)-( 5)) would be naturally satisfied.…”

Section: B Solution Encodingmentioning

confidence: 99%

“…But the search space of the problem grows exponentially as the problem size increases, leading to an inacceptable computational burden. To decrease the complexity of the problem and improve the search efficiency in large-scale request scenarios, several problem decomposition methods, for example the column generation [9], Lagrange relaxation [10], cut plane [11], are adopted to find the tight bounds of the problem.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

Xiong

Zheng

2022

IEEE Access

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Most existing evolutionary approaches to satellite range scheduling seek optimal solution in terms of the request satisfaction. The scheduling demand of managing ground station resource is seldom considered, which restricts their real-world applications. To effectively generate a set of more rational satellite range schedules, this paper establishes the multi-objective satellite range scheduling mathematical model. Unlike existing approaches, we propose a general population generation approach to solve the problem without relying on any specific kind of evolutionary algorithm, so different types of evolutionary algorithms can be extended to satellite range scheduling without modifying the original framework and search strategy. The idea is to utilize the request satisfaction and resource utilization knowledge learnt from parent schedules to guide the generation and updating of new solutions. Furthermore, an iterative rewriting operator is designed to guide a biased faster convergence towards the low request failure region in objective space. The proposed approach has been applied to five different types of classical and state-ofthe-art evolutionary algorithms and examined on benchmark problems. Experimental results illustrate the search efficiency enhancement and good adaptability to different evolutionary algorithms, which show the broad application prospect for satellite range scheduling.

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Section: B Solution Encodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

Xiong

Zheng

2022

IEEE Access

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“…In detail, a mixed integer programming model was suggested for scheduling optimization, and a dynamic programming algorithm with heuristics was developed. Furthermore, the exact solution algorithms based on mathematical programming also contain branch-and-bound [12], dynamic programming [21,22], Lagrangian relaxation [4,49] and Constraint programming [35].…”

Section: Introductionmentioning

confidence: 99%

Expectation and SAA Models and Algorithms for Scheduling of Multiple Earth Observation Satellites Under the Impact of Clouds

Wang

Demeulemeester²,

et al. 2020

IEEE Systems Journal

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In the explosively increased number of applications of earth observation satellites (EOSs), scheduling is a significative issue to satisfy more requests and obtain a high observation efficiency. This paper investigates the scheduling of multiple EOSs under the impact of clouds. Firstly, we formulate the presences of clouds as stochastic events, and propose an expectation model. Afterwards, for the first time, a branch-and-price algorithm based on Dantzig-Wolfe decomposition is devised to solve the model optimally and efficiently. In order to obtain initial columns for column generation, a dynamic programming algorithm and a heuristic algorithm are suggested, respectively. Furthermore, we discuss the impact of clouds in the case of joint probabilities, and establish a sample average approximation (SAA) model accordingly. With respect to the expectation model, numerical experiment results demonstrate the relative dominance of the proposed branch-and-price algorithm in terms of solution time compared to CPLEX. In addition, the solution of the proposed SAA model is proven to be more robust than that of the expectation model.

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“…Therefore in this paper, we aim to conduct a comprehensive survey of the AEOSSP and point out possible future research directions. Note that the range satellite scheduling problem (RSSP) [20,21,22], which schedules data downloads/communications between satellites and ground control stations, is another satellite scheduling problem similar to AEOSSP. We do not provide detailed review of RSSP, while the potential integrated model of AEOSSP and RSSP will be discussed in Section 4.2.…”

Section: Introductionmentioning

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 Heuristic Approach to Satellite Range Scheduling With Bounds Using Lagrangian Relaxation

Cited by 14 publications

References 10 publications

Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

Expectation and SAA Models and Algorithms for Scheduling of Multiple Earth Observation Satellites Under the Impact of Clouds

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

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