Dynamic construction of statistical models in managerial DSS

The fields of operations research (OR) and artificial intelligence (AI) provide complementary methods that may be combined into managerial decision support systems (DSS). However, the management domain is substantially different from domains in which prior expert systems have been developed. Consequently, successful application of OR/AI techniques in managerial DSS requires careful analysis and additional development. Ongoing research concerning design and implementation of managerial DSS is discussed. A prototype system capable of constructing linear statistical models of direct and indirect relationships from a knowledge base of relationships is described and evaluated.     

Input/output selection in DEA under expert information,with application to financial markets

Data envelopment analysis (DEA), as generally used, assumes precise knowledge regarding which variables are inputs and outputs; however, in many applications, there exists only partial knowledge. This paper presents a new methodology for selecting input/output variables endogenously to the DEA model in the presence of partial (or expert’s) knowledge by employing a reward variable observed exogenous to the operation of the DMUs. The reward is an allocation of a limited resource by an external agency, e.g. capital allocation by a market, based on the perceived internal managerial efficiencies. We present an iterative two-stage optimization model which addresses the benefit of possibly violating the expert information to determine an optimal internal performance evaluation of the DMUs for maximizing its correlation with the reward metric. Theoretical properties of the model are analyzed and statistical significance tests are developed for the marginal value of expert violation. The methodology is applied in Fundamental Analysis of publicly-traded firms, using quarterly financial data, to determine an optimized DEA-based fundamental strength indicator. More than 800 firms covering all major sectors of the US stock market are used in the empirical evaluation of the model. The firms so-screened by the model are used within out-of-sample mean-variance long-portfolio allocation to demonstrate the superiority of the methodology as an investment decision tool.     

基于熵的群决策专家选择研究

群决策是研究多人如何做出统一的有效抉择.在群决策中专家决策水平直接反应到决策结论上,是影响事业的成败关键,因此专家的选择对于群决策而言是至关重要的.建立了评价群决策专家的指标集,从专家选择角度定义了理想专家,给出了专家选择的熵模型,并通过一个例子说明该模型的有效性.     

一个用于支持经济预测决策的EP—ESS模型

本文论述了EP-ESS的结构及其各部分之间的关系,并给出了ES部分推理的数学模型及其实现的流程图。     

气相色谱专家系统的进展

综述了国家色谱研究分析中心在气相色谱专家系统方面的进展,着重于色谱分离模式的推荐、气相色谱柱系统的推荐、气相色谱操作条件的最佳化、气相色谱在线定性定量分析四个方面;列举了在石化部门和环境监测中气相色谱专家系统应用的大量成功实例,以说明软件的实用性。     

基于ACARS的飞机实时监控系统研究

为实现对飞机运行状况的实时监控,提出利用ACARS空地数据链下载的实时维护数据,结合飞机维修技术文档以及历史维护经验来建立飞机实时监控系统,以期快速准确地诊断故障．该系统有利于地面维护人员实时掌握飞机及发动机的运行状态信息,尽早地发现飞机故障,在飞机落地前准备故障解决方案,提高维修水平和排故效率．     

基于专家－模糊PID的通用温度控制系统研究

为了解决温度控制系统易受外界干扰、自身参数时变不确定以及控制难度大等问题, 采用了模糊PID控制算法结合专家判断, 组成专家－模糊PID控制器的方法来控制温度. 将该方法应用于通用智能温度控制器中, 调试运行结果表明: 该方法解决了常规PID适应性差、不能在线进行参数自整定的问题, 不仅具有PID控制精度高的优点, 同时具有良好的动、静态特性, 可满足在通用智能温度控制器中的应用     

基于理想排序的群组G2赋权方法研究

合理确定专家权重是群组赋权的关键。本文在分析同类研究的基础上,提出了基于理想排序的群组G2方法来确定专家权重。论文的主要特色是根据循环修正的思路利用平均值法、Board法和Copealand法对通过一致性检验的专家排序进行重排序,从而确定评价指标的理想排序,然后通过比较各通过一致性检验的专家排序和理想排序的相关性的大小来确定专家权重,保证了专家权重合理的反映了专家差异,保证了指标最终权重反映出群体决策思想。     

Optimality of the Expert Rule Under Partial Information

We study the uncertain dichotomous choice model. In this model a group of decision makers is required to select one of two alternatives. The applications of this model are relevant to a wide variety of areas, such as medicine, management and banking. The decision rule may be the simple majority rule; however, it is also possible to assign more weight to the opinion of members known to be more qualified. The extreme example of such a rule is the expert decision rule. We are concerned with the probability of the expert rule to be optimal. Our purpose is to investigate the behaviour of this probability as a function of the group size for several rather general types of distributions. One such family of distributions is that where the density function of the correctness probability is a polynomial (on the interval [1/2,1]). Our main result is an explicit formula for the probability in question. This contains formerly known results as very special cases.     

Modelling,making inferences and making decisions: The roles of sensitivity analysis

Sensitivity analysis, robustness studies and uncertainty analyses are key stages in the modelling, inference and evaluation used in operational research, decision analytic and risk management studies. However, sensitivity methods -or others so similar technically that they are difficult to distinguish from sensitivity methods- are used in many different circumstances for many different purposes; and the manner of their use in one context may be inappropriate in another. Thus in this paper, I categorise and explore the use of sensitivity analysis and its parallels, and in doing so I hope to provide a guide and typology to a large growing literature.