分布式调度三级CLOS网络中交换机制的选择

以分布式调度三级Clos网络为重点,对定长信元和变长分组两种交换方式性能和实现成本进行了对比分析;通过对网络第一级负载分配原则的理论分析,给出了变长分组交换方式下网络稳定性的条件,利用仿真分析对比了两种交换方式的时延特性.给出了分布式调度三级Clos网络中交换机制的选择原则.     

基于MPI的主从式并行MCMC

马尔可夫链蒙特卡洛(MCMC)是贝叶斯网络(BN)的一种近似推理算法,为保证其时间性能,在生成马尔可夫链的组成序列时,通常只使用较少的样本,其推理精度较低.以Gibbs抽样为基本框架,提出一种BN的并行MCMC推理方法PMCMC,在生成马尔可夫链的组成序列时,通过增加对BN中每个节点的抽样频率来提高推理精度,并在消息传递接口MPJ的支持下,利用主从式并行机制来实现其推理过程,保证了推理的时间性能.在3个不同BN上的推理实验结果表明,PMCMC在提高推理精度的同时有效保证了推理的时间生能.     

基于蚁群优化的物流网络多约束路径规划

面向大范围、大规模的军用物资调度,为保障任务规划满足多个约束的可行路径,是NP-C问题.本文在详细分析物流保障网络传输属性的基础上,给出了约束参数的完整描述,建立了多约束路径规划数学模型,设计了一种带综合启发因子的蚁群优化算法.仿真实验表明,该算法搜索效率高,具有全局收敛性,经过有限次迭代能够获得可行解,对网络规模和约束参数选择具有较好的灵活性和扩展性.     

不确定的多商品流多准则供应链网络模型

基于供应链网络中同层成员的相互竞争以及不同层成员间的相互合作关系,构建了不确定条件下由制造商、分销商和零售商组成具有多准则决策的多商品流供应链网络模型.研究了制造商和分销商的多准则决策,零售商在市场需求不确定条件下进行决策,同时考虑了制造商和零售商之间的直销关系.利用变分不等式的形式给出了供应链网络中各层以及整体的均衡条件和相应的经济解释.最后,通过算例说明了模型的合理性.     

基于改进神经网络的多AUV全覆盖路径规划

针对三维环境下的多自主水下机器人(Autonomous Underwater Vehicle,AUV)全覆盖路径规划问题,提出一种基于改进神经网络—Glasius生物启发神经网络（Glasius Bio-inspired Neural Network,GBNN）的全覆盖路径规划算法。对AUV的水下工作环境构建离散的三维栅格地图;根据栅格地图,建立相对应的三维GBNN模型;根据GBNN活性值的动态变化,AUV规划各自的搜索路径,对水下任务区域进行全覆盖搜索。仿真结果表示,多AUV可以协同完成覆盖搜索任务,能够自动避开各类静态和动态的障碍物,自动逃离路径的死锁区。     

城市交通连续平衡网络设计问题的模拟退火算法

对城市交通连续平衡网络设计问题作一简要介绍。建立该问题的双层规划模型。针对该问题．构建一种特殊的邻城结构．并设计出基于此邻城结构的模拟退火算法。通过一个实例对算法进行验证．并同其他算法进行比较．结果表明模拟退火算法的计算结果较精确．但相应的计算量增加。但该算法用于大型网络设计问题时．其优势则会有明显体现。     

城市交叉路口智能控制系统的研究

综合利用图的染色理论、模糊控制和模糊神经网络方面的知识，系统地从相位设计、实时模糊和定时控制以及不断改进控制规则这三个层次上来逐步深入地全面优化城市交叉路口处车流的疏导，使之更具智能性。     

基于模糊C均值聚类和神经网络的短时交通流预测方法

短时交通流预测是动态交通控制和诱导的前提。提出一种模糊C均值聚类和神经网络相结合的短时交通流预测方法。用同一组实测数据对比计算了该方法与BP神经网络预测方法、模糊神经网络预测方法分别得到的预测结果。计算结果表明：所提出的方法的预测准确性明显地高于其他两种方法。     

DECISION MAKING - THE ANALYTIC HIERARCHY AND NETWORK PROCESSES (AHP/ANP)

This is the first part of an introduction to multicriteria decision making using the Analytic Hierarchy Process (AHP) and its generalization, the Analytic Network Process (ANP). The discussion involves individual and group decisions both with the independence of the criteria from the alternatives as in the AHP and also with dependence and feedback in the entire decision structure as in the ANP. This part explains the Analytic Hierarchy Process, with examples, and presents in some detail the mathematical foundations. An exposition of the Analytic Network Process and its applications will appear in later issues of this journal.     

Fundamentals of the analytic network process — Dependence and feedback in decision-making with a single network

The Analytic Network Process (ANP) is a multicriteria theory of measurement used to derive relative priority scales of absolute numbers from individual judgments (or from actual measurements normalized to a relative form) that also belong to a fundamental scale of absolute numbers. These judgments represent the relative influence, of one of two elements over the other in a pairwise comparison process on a third element in the system, with respect to an underlying control criterion. Through its supermatrix, whose entries are themselves matrices of column priorities, the ANP synthesizes the outcome of dependence and feedback within and between clusters of elements. The Analytic Hierarchy Process (AHP) with its independence assumptions on upper levels from lower levels and the independence of the elements in a level is a special case of the ANP. The ANP is an essential tool for articulating our understanding of a decision problem. One had to overcome the limitation of linear hierarchic structures and their mathematical consequences. This part on the ANP summarizes and illustrates the basic concepts of the ANP and shows how informed intuitive judgments can lead to real life answers that are matched by actual measurements in the real world (for example, relative dollar values) as illustrated in market share examples that rely on judgments and not on numerical data. Thomas L. Saaty holds the Chair of University Professor, Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, and obtained his Ph.D. in mathematics from Yale University. Before that he was a professor at the Wharton School of the University of Pennsylvania for ten years. Prior to that he spent seven years at the Arms Control and Disarmament Agency in the State Department in Washington, DC, that carried out the arms reduction negotiations with the Soviets in Geneva. His current research interests include decision-making, planning, conflict resolution and synthesis in the brain. As a result of his search for an effective means to deal with weapons tradeoffs at the Disarmament Agency and, more generally, with decision-making and resource allocation, Professor Saaty developed The Analytic Hierarchy Process (AHP) and its generalization to dependence and feedback, the Analytic Network Process (ANP). He is co-developer of the software Expert Choice and of the software Super Decisions for decisions with dependence and feedback. He has authored or co-authored twelve books on the AHP/ANP. Professor Saaty has also written a number of other books that embrace a variety of topics, including Modern Nonlinear Equations, Nonlinear Mathematics, Graph Theory, The Four Color Problem, Behavioral Mathematics, Queuing Theory, Optimization in Integers, Embracing the Future and The Brain: Unraveling the Mystery of How It Works. His most recent book is Creative Thinking, Problem Solving & Decision Making. The book is a rich collection of ideas, incorporating research by a growing body of researchers and practitioners, profiles of creative people, projects and products, theory, philosophy, physics and metaphysics...all explained with a liberal dose of humor. He has published more than 300 refereed articles in a wide variety of professional journals. He has been on the editorial boards of Mathematical Reviews, Operations Research, Naval Research Logistics Quarterly, Mathematical and Computer Modeling, Socio-Economic Planning Sciences, Applied Mathematics Letters, and several others. He also served as consultant to many corporations and governments.