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两段探测目标的传感器任务调度问题0-1规划模型及算法
引用本文:李建平,张晗,罗永,朱承,何文涛.两段探测目标的传感器任务调度问题0-1规划模型及算法[J].国防科技大学学报,2017,39(3):121-129.
作者姓名:李建平  张晗  罗永  朱承  何文涛
作者单位:国防科学技术大学 理学院,国防科学技术大学 理学院,国防科大理学院
基金项目:国家自然科学基金项目(面上项目)面向人机团队的智能规划方法及实验研究 (61273322)
摘    要:为解决指挥系统控制中的调度困难,研究了一类特殊的传感器资源调度问。主要分析了跟踪目标的探测次数、时间间隔和传感器资源等约束条件。用跟踪目标的重要程度之和作为目标函数,建立了一个0-1规划的数学模型,再利用变换将其转化为0-1线性整数规划模型。利用割平面法求解得出最优调度策略,其能在工作量饱和的情况下合理调度传感器资源。为提高求解速度,提出了对应的模拟退火算法。通过对一些不同规模实例的求解,在资源利用率和算法的求解速度等指标上,与割平面法及遗传算法进行对比分析,验证了模型的有效性和模拟退火算法求解的高效性。

关 键 词:传感器  任务调度  数学建模    0-1规划  模拟退火算法
收稿时间:2016/5/5 0:00:00
修稿时间:2016/8/30 0:00:00

The 0-1 Programming Model And Algorithm For The Problem Of Sensor Task Scheduling For Double Detection
LI Jianping,ZHANG Han,LUO Yong,ZHU Cheng and HE Wentao.The 0-1 Programming Model And Algorithm For The Problem Of Sensor Task Scheduling For Double Detection[J].Journal of National University of Defense Technology,2017,39(3):121-129.
Authors:LI Jianping  ZHANG Han  LUO Yong  ZHU Cheng and HE Wentao
Institution:1. College of Science, National University of Defense Technology, Changsha 410073, China,1. College of Science, National University of Defense Technology, Changsha 410073, China,1. College of Science, National University of Defense Technology, Changsha 410073, China,2. The Key Laboratory of Information System Engineering, National University of Defense Technology, Changsha 410073, China and 2. The Key Laboratory of Information System Engineering, National University of Defense Technology, Changsha 410073, China
Abstract:This paper focuses on a special sensor scheduling problem which a target need to be detected two times in sequence and analyzes the constraint conditions including detected times, the interval between two detections, and the resource restrict of sensor. A 0-1 programming model is established and transformed to a 0-1 liner integer model whose objective function is the sum of degree of importance. The optimal solution is obtained by cutting plane algorithm. We propose a corresponding simulated annealing algorithm to improve the speed of solving and use it to solve some examples. We compare it with cutting plane algorithm and genetic algorithm by resource utilization and the speed of solving. It proves that the model is valid and the simulated annealing algorithm is high-efficiency.
Keywords:sensor  task scheduling  0-1 programming  simulated annealing algorithm  genetic algorithm
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