城市需水的智能预测方法研究

选取对城市用水影响较大的因素作为预测指标,通过HP滤波分析其指标及用水量的趋势成分及波动成分。应用多元线性回归法对趋势性成分进行模拟,应用模糊神经网络对波动性成分进行网络训练,以获得城市需水的总预测值。以大连市1980~2000年用水及其相关因子为例建模,以2001~2007年指标对模型进行检验,预测了大连市2010、2020年的需水值,为城市水资源规划提供一定的理论支持。     

北京市水资源状态与供需压力研究

针对北京市水资源短缺与用水需求不断增长的矛盾,将北京市水资源状态分成四个紧缺等级——无紧缺、轻度紧缺、中度紧缺和重度紧缺,形成了水资源状态指数,综合反映了北京市水资源动态变化;水资源供需压力分成四个压力等级——无压力、轻度压力、中度压力和重度压力,形成了供需压力指数,综合反映了北京市水资源供需状态。利用这两个指数对近年来北京市水资源状态和供需压力情况进行了综合评价。     

面向生态的陕西省渭河流域水资源合理配置

结合陕西省渭河流域的水资源开发利用现状,对其水资源优化管理进行研究。通过分析流域社会经济发展的特点及生态环境形势,以定额法为基础对2020年流域的生活、生产和生态需水进行了预测。根据系统概化图和配水目标,综合考虑水资源配置中的生态指标、社会指标和经济指标,建立了面向生态的水资源合理配置模型。将混沌优化算法与遗传算法相结合对模型进行求解,结果表明:陕西省渭河流域2020年75%保证率下的缺水率为18.1%。     

基于河道径流可变区间的河流水资源可开发利用率分析

为了确定水电开发活动对河流水资源的可开发利用率,建立分期展布的河道径流可变区间核算方法,其中河道径流包括河道最小生态需水和河道最大洪流,并以西藏拉萨河为例进行核算。结果表明:拉萨河河道最小生态需水和最大洪流年内动态变化分别为29.5~328.3 m3/s和95.1~1 673.4 m3/s。与河道最小生态需水约束相比,河道径流可变区间约束使得拉萨河年调节型水电开发的水资源可开发利用率从60.2%下降到18.7%。指出对于径流丰枯特征十分明显的季节性河流,大型水利工程在平衡径流季节分布的过程中,应该受河道径流可变区间约束,尤其是枯水期最大洪流约束下河道径流量的可增加空间。     

探讨计算机网络专业学生的实训教学课程设置

计算机网络专业的学生的实训内容通常是与今后的工作相关联的,这种关联的程度越高,说明实训的效果越好,也可以从一个侧面说明教学越成功。所以,实训项目不应该是凭空设想的,也不应该是几年如一日的照搬照抄的,应该是符合市场需求的,与时俱进的。     

淮河流域水质污染时空变异特征分析

选取淮河流域的82个水质监测站,对各站点的1986—2005年水质监测数据进行统计分析,探讨了全流域内水体污染物浓度变化的时空变异特征,为淮河流域水污染治理、水环境保护以及生态修复提供依据。采用时间序列法分析水体污染物浓度的时间变化规律,应用Mann-Kendall检验法对流域范围内水体污染物浓度变化趋势进行了分析。研究结果表明,淮河流域水质变化主要受到入河排污量、上游来水量、闸坝调控方式以及气候条件等方面因素的影响。蚌埠站的水体污染物浓度多年变化规律表明,1995年是水体污染物浓度变化的转折点,1995年前水体污染物浓度不断恶化,1995年后水体污染物浓度逐渐好转。DO浓度的年内变化主要受到水温的影响,表现为冬季浓度高于夏季浓度;CODMn浓度同时受到闸坝调控方式以及区域来水量的影响,汛期浓度低于非汛期。从全流域的水体污染物浓度变化规律看,有机污染物浓度呈显著上升趋势的河段主要分布在淮北支流上,说明在20世纪90年代后期,虽然流域进入相对丰水期以及进行了大规模的水污染联防工作,淮河流域水质污染得到了一定程度的改善。但在2000年后,随着流域内入河污水量和污染物排放量的增加,淮河流域的水质污染依然严重。     

Optimal models for single-period supply chain problems with fuzzy demand

The aim of this paper is to develop an optimal technique for dealing with the fuzziness aspect of demand uncertainties. Triangular fuzzy numbers are used to model external demand, and decision models in both non-coordination and coordination situations are constructed. It is shown that in the decision models there exists a unique solution that can be expressed analytically. Based on the closed form solutions for both models, the behaviors and relationships of both the manufacturer and the retailer are quantitatively analyzed, and a cooperative policy for the optimization of the whole supply chain is put forward.     

The solution and duality of imprecise network problems

Duality properties have been investigated by many researchers in the recent literature. They are introduced in this paper for a fully fuzzified version of the minimal cost flow problem, which is a basic model in network flow theory. This model illustrates the least cost of the shipment of a commodity through a capacitated network in terms of the imprecisely known available supplies at certain nodes which should be transmitted to fulfil uncertain demands at other nodes. First, we review on the most valuable results on fuzzy duality concepts to facilitate the discussion of this paper. By applying Hukuhara’s difference, approximated and exact multiplication and Wu’s scalar production, we exhibit the flow in network models. Then, we use combinatorial algorithms on a reduced problem which is derived from fully fuzzified MCFP to acquire fuzzy optimal flows. To give duality theorems, we utilize a total order on fuzzy numbers due to the level of risk and realize optimality conditions for providing some efficient combinatorial algorithms. Finally, we compare our results with the previous worthwhile works to demonstrate the efficiency and power of our scheme and the reasonability of our solutions in actual decision-making problems.     

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models. Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University, Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles at international journals. He is currently working on applications of artificial intelligence and adaptive control theory in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor manufacturing. Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management, inventory control, production planning and scheduling. Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively. From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis, and ubiquitous logistics information systems. An erratum to this article can be found at     

基于数据仓库技术的高校招生决策支持系统的构建

该文首先介绍了决策支持系统的概念,然后通过对高校招生进行需求分析,利用微软的产品MSSQLServer2005构建了招生决策支持系统平台。