Optimal scheduling of multiple Sun-synchronous orbit satellites refueling

Li, Chengzhang; Xu, Bo

doi:10.1016/j.asr.2020.03.049

Cited by 12 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The metaheuristic method is a popular approach for solving the MEOSS problem, which is inspired by general phenomena or specific domain knowledge [13] , including genetic algorithms [14] , adaptive large neighborhood search [15] , tabu search [16] , and particle swarm optimization [17] . In addition, Lagrangian relaxation [18] and exact algorithms (such as branch and bound [19,20] , and dynamic programming methods [21] ) have also been employed to simplify the problem-solving procedure and obtain an optimal solution. Notably, Wang et al [22] formulated the MEOSS problem as an unrelated parallel machine scheduling problem, then developed a novel preprocessing algorithm to effectively reduce the problem size.…”

Section: Introductionmentioning

confidence: 99%

“…Only a handful of researchers have investigated task observation and download scheduling. Marinelli et al [18] investigated satellite range scheduling with resource constraints and proposed a time-indexed 0-1 programming formulation. Although a Lagrangian relaxation heuristic was introduced to reduce variable spaces in their model and solve the problem, satellite energy and memory storage consumption were not considered.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Fast Insertion Tabu Search with Conflict-Avoidance Heuristic for the Multisatellite Multimode Crosslink Scheduling Problem

Yang,

He,

Liu

et al. 2024

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

An agile earth-observing satellite equipped with multimode cameras capable of transmitting observation data to other satellites is developed to rapidly respond to requests with multiple observation modes. This gives rise to the Multisatellite Multimode Crosslink Scheduling (MMCS) problem, which involves allocating observation requests to agile satellites, selecting appropriate timing and observation modes for the requests, and transmitting the data to the ground station via the satellite communication system. Herein, a mixed integer programming model is introduced to include all complex time and operation constraints. To solve the MMCS problem, a two-stage heuristic method, called Fast insertion Tabu Search with Conflict-avoidance (FTS-C) heuristic, is developed. In the first stage, a conflict-avoidance insertion algorithm is designed to generate a high-quality initial solution by considering the requests transmission and download. Further, the tabu search-based second stage optimizes the initial solution. Finally, an extensive empirical study based on a real-world situation demonstrates that FTS-C can generate a solution with higher quality in less time than other state-of-the-art algorithms and the CPLEX solver.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fast Insertion Tabu Search with Conflict-Avoidance Heuristic for the Multisatellite Multimode Crosslink Scheduling Problem

Yang,

He,

Liu

et al. 2024

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Vehicle refueling problems have been extensively investigated [25,27,36,42,43,56,57,62,[69][70][71]. One of the lines of study is the fixed route vehicle refueling problem (FRVRP), where the goal is to select the refueling points on a fixed route.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for the traveling salesman problem with refueling

Ottoni

Nepomuceno

Oliveira

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

The traveling salesman problem (TSP) is one of the best-known combinatorial optimization problems. Many methods derived from TSP have been applied to study autonomous vehicle route planning with fuel constraints. Nevertheless, less attention has been paid to reinforcement learning (RL) as a potential method to solve refueling problems. This paper employs RL to solve the traveling salesman problem With refueling (TSPWR). The technique proposes a model (actions, states, reinforcements) and RL-TSPWR algorithm. Focus is given on the analysis of RL parameters and on the refueling influence in route learning optimization of fuel cost. Two RL algorithms: Q-learning and SARSA are compared. In addition, RL parameter estimation is performed by Response Surface Methodology, Analysis of Variance and Tukey Test. The proposed method achieves the best solution in 15 out of 16 case studies.

show abstract

“…The second is M2M mode. Li et al studied the on-orbit refueling problem with the background of the fuel station and refueling spacecraft [20], optimized the task allocation and refueling sequence, and obtained the on-orbit refueling scheme meeting the constraints of time and carrying capacity. Zhang et al and Zhu et al considered the location of the refueling station and the cost of an open refueling station; ant colony algorithm or clustering algorithm was used to determine the location and number of fuel stations to meet the on-orbit refueling task constraints [21,22].…”

Section: Introductionmentioning

confidence: 99%

Multisatellite Flyby Inspection Trajectory Optimization Based on Constraint Repairing

et al. 2021

View full text Add to dashboard Cite

With the rapid development of on-orbit services and space situational awareness, there is an urgent demand for multisatellite flyby inspection (MSFI) that can obtain information about a large number of space targets with little fuel consumption in a short time. There are two kinds of constraints, namely inspection constraints (ICs) at each flyby point and transfer process constraints (TPCs) in the actual mission. Further considering the influence of discrete and continuous variables such as inspection sequence, time, and maneuver scheme, it is complex and difficult to solve MSFI. To optimize it efficiently, the task flow and the problem model are defined firstly. Then, the algorithm framework based on constraint repairing is given, which contains repair methods of the ICs and the TPCs. Finally, the proposed method is compared with the nonrepair optimization method in two numerical examples. The results indicate that when the constraints are hard to meet, it is better and more efficient than the nonrepair method.

show abstract

Optimal scheduling of multiple Sun-synchronous orbit satellites refueling

Cited by 12 publications

References 17 publications

A Fast Insertion Tabu Search with Conflict-Avoidance Heuristic for the Multisatellite Multimode Crosslink Scheduling Problem

A Fast Insertion Tabu Search with Conflict-Avoidance Heuristic for the Multisatellite Multimode Crosslink Scheduling Problem

Reinforcement learning for the traveling salesman problem with refueling

Multisatellite Flyby Inspection Trajectory Optimization Based on Constraint Repairing

Contact Info

Product

Resources

About