A branch-and-price algorithm for nanosatellite task scheduling to improve mission quality-of-service

Rigo, Cezar Antônio; Seman, Laio Oriel; Camponogara, Eduardo; Filho, Edemar Morsch; Bezerra, Eduardo Augusto; Munari, Pedro

doi:10.1016/j.ejor.2022.02.040

Cited by 14 publications

(3 citation statements)

References 56 publications

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“…The algorithm incorporated a dynamic programming algorithm to decompose the task and apply it to large-scale tasks in extended time. Comparing the proposed algorithm of the study with other algorithms, the algorithm was able to plan more complex tasks in an optimal manner within a reasonable time [17]. A recursive search artificial bee colony technique was proposed by Gao and other researchers to solve the scheduling optimization problem of concurrent testing activities.…”

Section: Related Workmentioning

confidence: 99%

Modeling and simulation of complex emergency dispatch based on BIPSO

Sun,

Huang

2024

Int. J. Simul. Multidisci. Des. Optim.

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In emergency task scheduling, this study proposes a complex model for emergency scheduling. It is based on the particle swarm algorithm and improves upon the traditional version. Additionally, the study recommends the use of the binary particle swarm optimization algorithm (PSO). The study proposes applying the multi-objective task scheduling-particle swarm optimization algorithm (MOTS-PSO) to the complex emergency scheduling model by combining it with the multi-objective function. Compared to other algorithms, the proposed improved algorithm exhibited the lowest average number of iterations, which consistently fell within the range of 130, and achieved a 100% success rate for optimization searches on the majority of functions. When compared with other models, the proposed research model demonstrated superior performance, exhibiting the lowest total scheduling cost, total execution time, and data transfer time of 280 and 900, respectively, for the task quantity of 5000. Furthermore, the proposed model exhibited the lowest maximum execution cost for a single node, which remained within the range of 0.45S. The outcomes of the experiments demonstrate that the proposed research model adequately satisfies the requirements for complex scheduling and its simulability has been confirmed.

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Section: Related Workmentioning

confidence: 99%

Modeling and simulation of complex emergency dispatch based on BIPSO

Sun,

Huang

2024

Int. J. Simul. Multidisci. Des. Optim.

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“…They derived a set of heuristics and dynamic programming solutions to overcome the computation burden of solving the linear program, which were addressed shortly after using ILP methods in Bensana et al [5]. More recently, the allocation of data pickup tasks has been achieved through some form of exact or inexact optimization, either Mixed Integer Linear Programming (MILP) [3,36], Dynamic Programming (DP) [31] or some combination of the two [10]. Justification for using MILP approaches is the desire to reach a globally optimal solution for the allocation of a set of tasks that are known prior to completing the scheduling procedure.…”

Section: Task Scheduling In Satellite Networkmentioning

confidence: 99%

A Delay-Tolerant Network Approach to Satellite Pickup and Delivery Scheduling

Lowe¹,

Clark²,

McGrath

et al. 2023

Preprint

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“…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%

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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A branch-and-price algorithm for nanosatellite task scheduling to improve mission quality-of-service

Cited by 14 publications

References 56 publications

Modeling and simulation of complex emergency dispatch based on BIPSO

Modeling and simulation of complex emergency dispatch based on BIPSO

A Delay-Tolerant Network Approach to Satellite Pickup and Delivery Scheduling

Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

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