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Scheduling Optimization for Imagery Satellite Constellations Using Column Generation.

机译:使用列生成的影像卫星星座调度优化。

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摘要

Imagery satellites have served many purposes ranging from national security missions to agricultural and environmental monitoring. The need to schedule imagery collections for satellites has been important since their inception; however, the constellations have only included a few satellites. The full constellation of the satellites represented in this research will be a leap in capacity that has only recently been considered. The fundamental question for the multi-satellite collection scheduling problem involves three inter-related considerations: 1) which targets should be imaged; 2) which satellite should image each target; and 3) when should each imaging task begin.;Numerous methods have been used to address the current collection scheduling problem, including simulated annealing, genetic algorithms, tabu search, variations of greedy algorithms, and constraint programming, with the general consensus being that pure integer programming is not practical for realistically-sized problems due to their highly combinatorial nature.;The methodology used in this research is a unique approach to the large multi-satellite collection scheduling problem that had not been previously investigated. This novel approach involves three sequential steps, "cluster-route-schedule". The first step, "cluster", groups imaging targets into clusters using a statistical method, based on a variety of parameters. The second step, "route", uses the decomposition method of column generation to determine which clusters each of the satellites should image throughout its orbital revolution, and when. The third step, "schedule", schedules the individual imaging targets within each cluster, using time-space networks and heuristics.;This research provides an integer programming-based method for scheduling imagery collection for large multi-satellite constellations that employ innovative technological advances in satellite maneuverability. The methodology is extendable to other types of satellite constellations (including fewer or more satellites), and possibly to some scheduling and routing problems in general.
机译:从国家安全任务到农业和环境监测,图像卫星已达到许多目的。自卫星诞生以来,为卫星安排图像收集工作就变得很重要;但是,这些星座只包括了几颗卫星。这项研究中所代表的卫星的全部星座将是容量的飞跃,直到最近才考虑到这一点。多卫星收集调度问题的基本问题涉及三个相互关联的考虑因素:1)应该对哪些目标成像。 2)哪颗卫星应该为每个目标成像? 3)每个成像任务应在何时开始。已经使用了许多方法来解决当前的采集调度问题,包括模拟退火,遗传算法,禁忌搜索,贪婪算法的变体和约束编程,通常的共识是纯整数规划由于具有高度的组合性,因此对于实际大小的问题不切实际。本研究中使用的方法是以前从未研究过的解决大型多卫星收集调度问题的独特方法。这种新颖的方法涉及三个连续步骤,即“集群路由计划”。第一步是“聚类”,它基于各种参数,使用统计方法将成像目标分为几类。第二步“路线”,使用列生成的分解方法来确定每个卫星在其轨道旋转中应成像的群集以及何时成像。第三步,“计划”,使用时空网络和启发式方法,对每个聚类中的单个成像目标进行计划。;本研究提供了一种基于整数编程的方法,用于利用创新技术为大型多卫星星座安排图像采集在卫星可操纵性方面。该方法可以扩展到其他类型的卫星星座(包括更少或更多的卫星),并且可能扩展到一般的某些调度和路由问题。

著录项

  • 作者

    Nelson, Francine N.;

  • 作者单位

    George Mason University.;

  • 授予单位 George Mason University.;
  • 学科 Operations Research.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 188 p.
  • 总页数 188
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:42:53

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