概率语言多属性群决策方法及其在新型智慧城市市民获得感评价中的应用

针对公众参与的语言信息多属性群决策问题,研究了考虑参与者满意度的概率语言多属性群决策方法。首先,根据参与者的语言评价信息确定并规范化概率语言决策矩阵。然后,对大群体进行共识分析,由最大化参与者群体的满意度构建线性规划模型,确定参与者群组的权重;构造正、负理想方案的评价向量,构建多目标规划模型,用拉格朗日乘子法求解属性权重;定义各方案的加权贴近度,并以此对方案进行排序和优选。最后,通过新型智慧城市市民获得感评价案例验证了模型的可行性和有效性。     

出生率与年龄段有关的生灭分枝树

本文基于生灭过程的生灭演化机理, 将生物繁衍过程描述为有向随机图过程-随机分枝树, 建立了出生率与年龄段有关的生灭分枝树演化模型. 本文研究了任一节点在不同年龄及临死时刻的出度分布、虚出度分布和拟出度分布, 并证明了拟出度过程是随机时刻终止的Poisson过程, 讨论了首生年龄及相对出生年龄的分布, 给出了任一节点成为孤立节点的概率.     

模糊多属性群决策一致性分析研究

群决策过程中各成员可能以不同的方式给出决策信息,讨论了四种不同决策信息的统一方法,设置了模糊环境下进行一致性判断的准则,介绍了梯形模糊数在群决策过程中的运算方法,举例说明了这种方法的应用步骤.     

Modelling decision making and learning by low latitude hunter gatherers

Recent anthropological studies have demonstrated that low latitude ‘encounter’ foragers exploit their environments in energetically very efficient manners and closely track the environment as it changes. The paper begins to investigate how they manage to do this by proposing a simple decision making and learning rule developed from an evolutionary ecological basis. Having described the mathematical model the paper refers to simulation studies exploring this model which suggest that some of the seemingly complex aspects of hunter gatherer behaviour may result from the use of simple decision making and learning processes.     

Interaction of trip decisions and traffic systems dynamics

A major difficulty in the study of large-scale complex systems involving human decision-making with non-linear collective effects is that of obtaining pertinent data at the desired level of detail. A controlled experiment involving real commuters in a hypothetical computer simulated traffic system is described as an alternative approach of conducting observational studies to support the modelling of such complex dynamic interactive decision systems. An overview of the principal characteristics of the traffic system's evolution obtained in this experiment is presented. Also illustrated are the features of a proposed behavioral framework where users are viewed as boundedly rational seekers of a satisfactory choice outcome.     

一类T形树匹配唯一的充要条件

证明：若m∈Ze^ ,则T形树T(1,m,n）匹配唯一当且仅当n≠m,m 3,2m 5.     

一种新型风险型多指标决策方法研究

传统的风险型多指标决策模型没有考虑决策者对风险的态度,而决策者对风险的态度会影响决策的结果,针对这一问题文章在累积前景理论与灰色关联方法的基础上,提出一种考虑决策者风险偏好的风险型多指标决策的方法.该方法首先利用极差化法对风险决策矩阵进行规范化处理,并在此基础上构造出最优与最劣方案;然后利用累积前景理论与灰色关联方法构建前景值函数,并给出利用灰色关联思想确定指标权重的方法与步骤;最终求出各个方案的综合前景值并进行排序选优.通过某电信运营商对管道资源建设方案选择的实例分析说明了方法的可行性与有效性.     

最大Randid指标的k悬挂点化学树的性质

化学分子图G的Randie指标为R（G）=∑wv（dG（u）dG（v））^2/1．其中uv是G的边,dG（u）表示的顶点u的度．本文刻画了具有最大Randie指标的k悬挂点化学树的一些性质．     

Single Sample Path-Based Optimization of Markov Chains

Motivated by the needs of on-line optimization of real-world engineering systems, we studied single sample path-based algorithms for Markov decision problems (MDP). The sample path used in the algorithms can be obtained by observing the operation of a real system. We give a simple example to explain the advantages of the sample path-based approach over the traditional computation-based approach: matrix inversion is not required; some transition probabilities do not have to be known; it may save storage space; and it gives the flexibility of iterating the actions for a subset of the state space in each iteration. The effect of the estimation errors and the convergence property of the sample path-based approach are studied. Finally, we propose a fast algorithm, which updates the policy whenever the system reaches a particular set of states and prove that the algorithm converges to the true optimal policy with probability one under some conditions. The sample path-based approach may have important applications to the design and management of engineering systems, such as high speed communication networks.This work was supported in part by     

Scenario Reduction Algorithms in Stochastic Programming

We consider convex stochastic programs with an (approximate) initial probability distribution P having finite support supp P, i.e., finitely many scenarios. The behaviour of such stochastic programs is stable with respect to perturbations of P measured in terms of a Fortet-Mourier probability metric. The problem of optimal scenario reduction consists in determining a probability measure that is supported by a subset of supp P of prescribed cardinality and is closest to P in terms of such a probability metric. Two new versions of forward and backward type algorithms are presented for computing such optimally reduced probability measures approximately. Compared to earlier versions, the computational performance (accuracy, running time) of the new algorithms has been improved considerably. Numerical experience is reported for different instances of scenario trees with computable optimal lower bounds. The test examples also include a ternary scenario tree representing the weekly electrical load process in a power management model.