| 资源约束突变的航天器观测快速重调度优化算法 |
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| 作者姓名： | 赵凡宇 徐瑞 崔平远 |
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| 作者单位： | 北京理工大学 深空探测技术研究所, 北京 100081;飞行器动力学与控制教育部重点实验室, 北京 100081,北京理工大学 深空探测技术研究所, 北京 100081;飞行器动力学与控制教育部重点实验室, 北京 100081,北京理工大学 深空探测技术研究所, 北京 100081;飞行器动力学与控制教育部重点实验室, 北京 100081 |
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| 基金项目： | 国家自然科学基金资助项目(60803051);高等学校博士学科点专项科研基金资助项目(20111101110001);北京理工大学创新团队 |
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| 摘 要： | 针对航天器对地观测调度中资源约束发生突变的情况,提出了一种基于蚁群算法的启发式重调度算法。首先对重调度过程中的资源约束进行分析,给出了资源约束发生变化的重调度模型。然后,结合原调度优化结果,给出重调度任务集合更新方法,对任务集合进行剪裁。最终,基于最大限度利用原调度方案信息的思想,结合任务集合更新及优先级等启发式信息,给出了一种改进的重调度优化算法。数值计算结果表明,所设计的算法可以快速有效的提高重调度过程的收益。
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| 关 键 词： | 航天器观测 重调度 蚁群优化 资源约束 |
| 收稿时间： | 2014/12/25 0:00:00 |
| 修稿时间： | 2015/4/30 0:00:00 |
Rescheduling Optimization for Spacecraft Observation with Resource Constraints Changing |
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| Authors: | ZHAO Fanyu XU Rui and CUI Pingyuan |
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| Institution: | Institute of Deep Space Exploration Technology, Beijing Institute of Technology, Beijing 100081, China;Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, Beijing 100081, China,Institute of Deep Space Exploration Technology, Beijing Institute of Technology, Beijing 100081, China;Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, Beijing 100081, China and Institute of Deep Space Exploration Technology, Beijing Institute of Technology, Beijing 100081, China;Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, Beijing 100081, China |
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| Abstract: | A rescheduling optimizing algorithm based on ant colony optimization (ACO) is proposed in this paper for the observing rescheduling with resource constraints changing. First, the resource constraints in the rescheduling process are analyzed, and a rescheduling model with resource constrains changing is established. Second, taking the advantages of the original scheduling results, an updating method is given out for the selecting of the missions. Finally, based on the principle of taking advantage of the original scheduling results as much as possible, combining the mission updating method and priorities of the missions, a heuristic rescheduling optimizing algorithm is proposed. The results show that the algorithm could effectively improve the profit of the rescheduling process. |
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| Keywords: | spacecraft observing rescheduling ant colony optimization resource constraints |
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