共查询到17条相似文献,搜索用时 343 毫秒
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一种新的实用弱化缓冲算子 总被引:14,自引:2,他引:14
本文以预测过程中常常出现的冲击扰动问题为出发点,讨论了弱化缓冲算子的基本性质.并以这些性质为研究基础,针对现有弱化算子不能解决的实际问题,构造了一个新的弱化缓冲算子,将该弱化算子应用到冲击扰动系统预测实际问题中,建立一个新的预测模型,结果表明,该预测模型取得了满意的拟合效果和预测效果.因此,这个新的弱化算子也同样是一个实用的弱化缓冲算子,具备了一定的使用价值. 相似文献
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在缓冲算子公理体系下,研究和构造了一类基于平均的强化缓冲算子,并比较了它们的内在关系和缓冲作用,实例验证了这类算子序列的有效性与实用性,有效解决了冲击扰动系统的行为数据在建模预测过程中常常出现的定性分析与定量预测不符的问题. 相似文献
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利用缓冲算子提高数据序列光滑性是提高灰色GM(1,1)模型预测精度的重要途径之一。在对缓冲算子和已有强化缓冲算子研究的基础上,构造了一类新的强化缓冲算子(strengthening buffer operator,SBO),有效地解决了冲击扰动数据序列在建模预测过程中常常出现的定量预测结果与定性分析结论不符的问题,实例分析结果表明:这类新的强化缓冲算子能显著提高数据预测模型的预测精度。 相似文献
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基于灰色系统理论的我国物流发展规模的预测研究 总被引:2,自引:0,他引:2
物流发展规模预测对于制定宏观经济政策和促进经济发展具有重要意义。在我国现代物流产业统计指标体系尚未健全的情况下.运用灰色系统理论思想与方法,能够在一定程度上解决物流产业量化研究的瓶颈问题。灰色系统预测模型GM(1,1)模型,为单序列建模。能够弱化序列随机性,挖掘系统演化规律.因此本文应用该模型对我国货运量进行预测,以此反映未来物流的发展规模.为国家规划物流产业和制定物流政策提供决策依据;同时研究结果表明该预测模型精度等级较高.效果较好.在基于灰色系统思想的中国现代物流产业的量化研究过程中.GM(1,1)预测模型的应用起到了抛砖引玉的作用。 相似文献
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本文运用缓冲算子和灰色GM(1,1)模型,对中国能源消费总量和单位GDP能耗进行了模拟和预测,在能源消费预测结果的基础上,构建了两种控制策略模型,并以中国单位GDP能耗预测为例进行了算例分析。研究结果表明,灰色模型较好地模拟和预测了中国能源消费总量和单位GDP能耗。中国在“十二五”期间的节能潜力很大,能顺利完成能源消费总量的指标。“十二五”安全控制策略为[0.48,1),即国家在“十二五”期间的控制力度应调整为“十一五”控制力度的0.48~1倍之间。 相似文献
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基于动态扫描和蚂蚁算法的物流配送网络优化研究 总被引:4,自引:0,他引:4
本文在对动态扫描和蚂蚁算法研究的基础上,针对蚂蚁算法在求解大规模物流配送问题中存在的不足,利用动态扫描方法在区域选择方面的实用性和蚂蚁算法在局部优化方面的优点,提出综合两种方法的混合算法,并进行了实验计算.计算结果表明,混合算法获得了较满意的效果. 相似文献
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Online scheduling with a buffer on related machines 总被引:1,自引:1,他引:0
Online scheduling with a buffer is a semi-online problem which is strongly related to the basic online scheduling problem. Jobs arrive one by one and are
to be assigned to parallel machines. A buffer of a fixed capacity K is available for storing at most K input jobs. An arriving job must be either assigned to a machine immediately upon arrival, or it can be stored in the buffer
for unlimited time. A stored job which is removed from the buffer (possibly, in order to allocate a space in the buffer for
a new job) must be assigned immediately as well. We study the case of two uniformly related machines of speed ratio s≥1, with the goal of makespan minimization. 相似文献
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This paper proposes an efficient heuristic algorithm to solve the problem of allocating the buffers in a production line for given total buffer capacity and the minimum required throughput. While the traditional research has focused on the problem of allocating the buffer capacity to maximize the throughput, the objective of minimizing the average Work-In-Process (WIP) is considered. Minimization of the WIP in the production line has drawn much attention in many practical applications, due to the growing efforts in reducing the production lead times for faster customer response. Two heuristics are proposed and compared to solve the problem. One is a very simple heuristic approach, and the other is a modified 'Non-SEVA' (Non-Standard Exchange Vector Algorithm) which was originally proposed by Seong et al . ( International Journal of Production Research, 33, 1989-2005, 1995) for the throughput maximization problem. Through computational tests, the efficiency of the proposed approach is shown. 相似文献
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经典关键链项目缓冲确定方法只考虑了一级链路上活动的信息,导致最终计算出的缓冲量不准确。基于此,本文首先通过对项目计划进行分形调整,利用一维关联维数确定不同级别链路之间的相关性,然后利用Logistic增长模型构建缓冲模型,将缓冲的确定分散到分形网路中相关链路的各个活动上,通过利用逆向选择原理确定均衡缓冲截取位置,并进一步确定项目缓冲。最后将本文方法与C&PM,RSEM,APD三种方法进行仿真实验对比分析,实验结果表明,本文方法能够在保证按时完工率的前提下,有效缩短项目工期,并降低项目总成本。 相似文献
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Minghui Zhang Xin Han Yan Lan Hing-Fung Ting 《Journal of Combinatorial Optimization》2017,33(2):530-542
In this paper we study the online bin packing with buffer and bounded size, i.e., there are items with size within \((\alpha ,1/2]\) where \(0 \le \alpha < 1/2 \), and there is a buffer with constant size. Each time when a new item is given, it can be stored in the buffer temporarily or packed into some bin, once it is packed in the bin, we cannot repack it. If the input is ended, the items in the buffer should be packed into bins too. In our setting, any time there is at most one bin open, i.e., that bin can accept new items, and all the other bins are closed. This problem is first studied by Zheng et al. (J Combin Optim 30(2):360–369, 2015), and they proposed a 1.4444-competitive algorithm and a lower bound 1.3333 on the competitive ratio. We improve the lower bound from 1.3333 to 1.4230, and the upper bound from 1.4444 to 1.4243. 相似文献