Exact and Heuristic Scheduling Algorithms for Multiple Earth Observation Satellites Under Uncertainties of Clouds

Exact and Heuristic Scheduling Algorithms for Multiple Earth Observation Satellites Under Uncertainties of Clouds
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云不确定性下多颗对地观测卫星精确启发式调度算法

DOI:
10.1109/jsyst.2018.2874223
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发表时间:
2015-07
影响因子:
4.4
通讯作者:
Liu Jin
Liu Jin
中科院分区:
计算机科学2区
文献类型:
--
作者:
Wang Jianjiang;Demeulemeester Erik;Hu Xuejun;Qiu Dishan;Liu Jin

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大多数地球观测卫星都配备了光学传感器,无法穿透云层。因此,由于云的存在,许多观测将是无用的。本文研究了云环境不确定性下的多个EOS调度问题。为了提高任务完成的可能性,我们考虑到每个任务调度到多个资源(轨道),建立了一个新的非线性数学模型。为了有效地求解该问题,提出了一种基于枚举的精确算法,该算法利用路径规划求解每个子问题,并将子问题的所有可行解组合起来求解主问题。此外,三个算法被设计来解决大规模的问题。从随机样本的实验结果来看,我们的模型的解决方案比以前的研究表现得更好。此外,我们的精确算法和混合整数非线性规划求解器-Couenne可以解决我们的模型最优的小问题,但我们的算法是更有效的比Couenne。对于大规模的问题,我们揭示了所提出的启发式算法的优点和缺点,同时解决各种大小的不同的情况。
Most earth observation satellites are equipped with optical sensors, which cannot see through clouds. Hence, many observations will be useless due to the presence of clouds. In this paper, we study the scheduling problem of multiple EOSs under uncertainties of clouds. In order to improve the possibility of completing tasks, we take the scheduling of each task to multiple resources (orbits) into account and establish a novel nonlinear mathematical model. To solve the problem efficiently, an exact algorithm based on enumeration is proposed, in which each subproblem is solved by path programming, and all the feasible solutions of subproblems are combined to solve the master problem. Furthermore, three heuristics are designed to solve the large-scale problems. From the experimental results on random samples, it is observed that the solutions of our model perform better than those of the previous studies. Besides, both our exact algorithm and a mixed-integer nonlinear programming solver-Couenne can solve our model optimally for small problems, but our algorithm is more efficient than Couenne. For large-scale problems, we reveal the strengths and weaknesses of the proposed heuristic algorithms while solving different instances of various sizes.
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