Application of the Fuzzy Weighted Average in Strategic Portfolio Management*

We propose a systematic approach that incorporates fuzzy set theory in conjunction with portfolio matrices to assist managers in reaching a better understanding of the overall competitiveness of their business portfolios. Integer linear programming is also accommodated in the proposed integrated approach to help select strategic plans by using the results derived from the previous portfolio analysis and other financial data. The proposed integrated approach is designed from a strategy‐oriented perspective for portfolio management at the corporate level. It has the advantage of dealing with the uncertainty problem of decision makers in doing evaluation, providing a technique that presents the diversity of confidence and optimism levels of decision makers. Furthermore, integer linear programming is used because it offers an effective quantitative method for managers to allocate constrained resources optimally among proposed strategies. An illustration from a real‐world situation demonstrates the integrated approach. Although a particular portfolio matrix model has been adopted in our research, the procedure proposed here can be modified to incorporate other portfolio matrices.     

基于有限合作机制的群决策熵模型研究

研究一类具有合作机制的群决策问题。提出两阈值的合作函数对决策人之间的合作关系进行模糊分类,采用模糊测度方法度量决策人和决策人集的权力指数,建立合作群决策的非线性规划熵模型集结权力指数,并基于极大熵的最优化原理求解该模型。利用Choquet模糊积分计算备选方案的综合评价值,并对备选方案排序选择最优方案。最后通过算例分析并验证合作群决策模型和运用模糊积分方法求解模型的合理性、有效性。     

基于VIKOR和诱导广义直觉梯形模糊Choquet积分算子的多属性群决策方法

针对属性值为直觉梯形模糊数,决策者间和属性间存在相互关联的多属性群决策问题,引入模糊测度和Choquet积分的概念,在直觉梯形模糊数的运算法则基础上构建了诱导型广义直觉梯形模糊choquet积分平均(IG-ITFCA)算子和诱导型广义直觉梯形模糊choquet积分几何(IG-ITFCG)算子,探讨上述算子的若干性质及一些特例,进而提出了基于诱导型广义直觉梯形模糊Choquet积分算子和多准则妥协优化解(VIKOR)的直觉梯形模糊多属性群决策方法。实例分析验证该方法的有效性和合理性。     

A fuzzy set approach for R&D portfolio selection using a real options valuation model

Making R&D portfolio decision is difficult, because long lead times of R&D and market and technology dynamics lead to unavailable and unreliable collected data for portfolio management. The objective of this research is to develop a fuzzy R&D portfolio selection model to hedge against the R&D uncertainty. Fuzzy set theory is applied to model uncertain and flexible project information. Since traditional project valuation methods often underestimate the risky project, a fuzzy compound-options model is used to evaluate the value of each R&D project. The R&D portfolio selection problem is formulated as a fuzzy zero–one integer programming model that can handle both uncertain and flexible parameters to determine the optimal project portfolio. A new transformation method based on qualitative possibility theory is developed to convert the fuzzy portfolio selection model into a crisp mathematical model from the risk-averse perspective. The transformed model can be solved by an optimization technique. An example is used to illustrate the proposed approach. We conclude that the proposed approach can assist decision makers in selecting suitable R&D portfolios, while there is a lack of reliable project information.     

Policies for knowledge refreshing in databases

Knowledge discovery in databases (KDD) provides organizations necessary tools to sift through vast data stores to extract knowledge. This process supports and improves decision making in organizations. In this paper, we introduce and define the concept of knowledge refreshing, a critical step to ensure the quality and timeliness of knowledge discovered in a KDD process. This has been unfortunately overlooked by prior researchers. Specifically, we study knowledge refreshing from the perspective of when to refresh knowledge so that the total system cost over a time horizon is minimized. We propose a model for knowledge refreshing, and a dynamic programming methodology for developing optimal strategies. We demonstrate the effectiveness of the proposed methodology using data from a real world application. The proposed methodology provides decision makers guidance in running KDD effectively and efficiently.     

一种决策者判断一致性的聚类方法

对于产量为模糊区间数的生产计划群决策问题,考虑不同产品生产的优先度和决策者权重对决策者判断一致性度量的影响,给出了相对加权一致度的一种计算方法。当群决策的结果不一致时,提出了依据相对加权一致度对决策者进行聚类的方法,并给出了每一类决策者决策结果的综合方法。最后通过算例说明了方法的应用过程。     

基于Heronian平均算子的Pythagorean模糊不确定语言决策方法

针对评价信息为Pythagorean模糊不确定语言数、属性权重未知且属性间相互影响的多属性决策问题,提出一种基于灰色关联法和Heronian平均算子的决策方法。首先,提出Pythagorean模糊不确定语言Heronian平均（PFULHM）算子和Pythagorean模糊不确定语言几何Heronian平均（PFULGHM）算子,并证明其满足幂等性、单调性、有界性及可交换性。考虑到属性权重之间重要程度的差异,定义了Pythagorean模糊不确定语言加权Heronian平均（PFULWHM）算子和Pythagorean模糊不确定语言加权几何Heronian平均（PFULWGHM）算子。其次,将灰色关联法运用到Pythagorean模糊不确定语言环境中求解属性权重。最后,提出基于PFULWHM算子和PFULWGHM算子的决策方法,并通过算例分析说明本文提出方法的有效性。     

A Multiperiod Audit Staff Planning Model Using Multiple Objectives: Development and Evaluation*

The audit staff planning problem, a specific type of manpower planning problem, has been modeled using goal programming and, more recently, multiple objective linear programming. Prior studies developed single-period models and did not go beyond the model building stage. This study develops a multiperiod audit staff planning model and evaluates the model using a test application involving actual decision makers (partners in public accounting firms). The multiperiod model includes seven objectives to be optimized: profit (to be maximized), late completion of work, work declined, staff augmentation, staff reduction, underutilization of the work force, and shortfall in meeting professional development targets (all to be minimized). Over a four-quarter planning horizon with one “busy season,” the model is subject to constraints with respect to the projected audit work load, ability to substitute personnel and to perform interim audit work, available staff hours (including overtime limitations), supervisory requirements, and professional development targets. Results of the test application showed that the model was capable of producing a range of values for each objective. The participants were exposed to much of that range when making their decisions. The results also showed that all objectives were important and that participants were consistent in choosing their preferred level of each objective over several runs of the model. These results and the reactions of the participants demonstrate that the model is usable by actual decision makers and has potential for a number of specific applications.     

基于犹豫模糊语言信息的前景决策方法

本文首先回顾了模糊信息下前景理论研究的现状,发现犹豫模糊语言这一决策中常见的信息表达形式在前景理论框架中的研究被忽略,同时犹豫模糊环境下的前景决策方法具有一定的应用背景;基于此,本文考虑到决策者在实际决策过程中惯用的信息表达以及面对收益和损失时不同的风险态度,试图在犹豫模糊语言环境下构造新的前景理论决策框架,建立基于犹豫模糊语言信息的前景决策方法,并给出具体的决策步骤;最后通过算例分析展示了该方法的实际应用过程,并与犹豫模糊语言环境下的期望效用决策结果进行对比,说明了该方法更符合实际决策情景。     

Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel‐Induced Pipeline Damage

Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel‐induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step‐by‐step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN‐based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel‐induced pipeline damage model is proposed to reveal the cause‐effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment.     

基于改进教学式方法的可解释信用风险评价模型构建

信用风险评价是金融机构风险防控的重要环节之一。近年来，基于机器学习的信用风险评价模型以其准确的预测效果受到越来越多的关注，但机器学习模型具有可解释性不强的弊端，导致投资者无法完全信任其预测结果。针对上述问题，本文提出了一种改进的教学式方法，利用机器学习模型指导生成一个兼顾准确性与可解释性的信用风险评价决策树模型，以辅助投资者决策。为提高决策树对机器学习模型中正确功能的学习能力，提出了基于Weight Synthetic Minority Over-sampling Technique（Weight-SMOTE）的伪数据集生成方法，以提高伪数据集中可信度高的功能所标记的伪样本比例；为实现所生成的决策树在准确性、可解释性以及其与机器学习模型一致性间的有效权衡，在决策树生成过程中提出了一种新的决策树剪枝方法；同时针对保真度评价指标的局限性，提出了真保真度评价指标，来有效的衡量决策树与机器学习模型正确功能的近似程度。最后使用3个真实信用风险评价数据集对改进的教学式方法进行验证，实验结果表明所提出方法能够生成准确且可解释的信用风险评价模型，以满足投资者的决策偏好与实际需求。     

一种基于TOPSIS的混合型多属性群决策方法

本文针对具有语言型和直觉模糊数两种评价信息的混合型多属性群决策问题,提出了一种基于TOPSIS的决策方法。首先,定义了新的函数,可将不同粒度的语言评价信息转换成直觉模糊数。其次,在直觉模糊数熵值的基础上,提出了一种新的专家权重确定模型。再次,利用IFWA算子在把个体决策矩阵集结为群体决策矩阵后,基于TOPSIS分别计算群体评价值到正理想解和负理想解的距离,从而得到方案集的排序。最后,在ERP选优问题中的应用,验证了方法的有效性。     

高校知识工作者的激励模型及模糊综合评价

知识经济下,高校作为知识传授和创新的桥头堡,如何极大地激励高校知识工作者的创新潜能和科教热情,成为中国高校的一大难题。本文在几种激励理论的基础之上,提出高校知识工作者的激励模型,并对其建立了模糊综合评价模型,为制定高校知识工作者的激励政策提供有用的依据。     

基于模糊综合评判收益约束的贷款组合优化模型

贷款组合优化是商业银行信贷管理中最常见的决策。本文分析了现有的贷款组合优化模型的特点和弊端,以组合风险最小化为目标,以模糊数学中的综合评判关系为约束条件,建立了贷款组合的模糊优化模型。在组合收益率的综合评判目标和评判矩阵已确定的前提条件下,利用二次规划的方法解出各类贷款额占总贷款额的比重,解决了银行各类贷款的组合决策问题。通过进一步的实例分析,又从实证角度说明了该方法的合理性和可行性。     

Power sharing at senior management levels

The paper presents research findings on decision making and differences in skill perception between 55 dyads of German managers of large organizations. These findings are related to research on carefully matched samples in the U.S. and U.K. The data, while still confined to two variables (two vertical and one horizontal method of measuring power sharing, and two ways of measuring perceptual differences between closely related decision makers), confirm the validity of a contingency model of managerial decision making and power sharing.     

Regression models of behavior for managerial decision making

A principal problem in systems studies concerns the development of models that will be accepted and used by decision makers in organizations. Regression models derived from managers' past behavior offer promise in overcoming this problem for many repetitive types of decisions. Employing a simulated production planning environment, this paper discusses both the potential usefulness and limitations of such models for understanding and improving decision making in practical applications.     

模糊信息下多种类应急物资多周期分配优化模型

考虑灾害的突发性、信息获取的不完全性以及应急救援的紧迫性，引入区间数描述应急物资供给与需求的模糊不确定性，引入三角模糊数刻画路网容量受限情况下每周期的最大物资运输总量，综合考虑灾害、灾区、灾民以及物资等多重因素，引入延迟系数，以应急物资分配的总延迟时间最小化和总系统损失最小化为目标，构建模糊信息条件下考虑多需求点、多配送中心、多物资、多周期、多目标的应急物资动态分配优化决策模型，分析了区间目标函数、区间模糊与三角模糊约束条件的清晰化方法，采用基于二维欧式距离客观赋权模糊算法求解模型，并以青海玉树地震为例对所提出模型的有效性和可行性进行验证。结果表明：所提出的模型能够最大程度地权衡延迟时间与系统损失，形成多周期最优的物资分配方案；现实多周期应急物资分配，时间并不是唯一考虑的因素，需要综合考虑不同应急周期的灾情、灾区、灾民和物资等多种因素对系统总损失造成的影响；重视时间偏好系数，可能使系统总损失增大，表明单一考虑时间偏好系数和损失偏好系数均具有片面性，应该把握选择"度"，发挥二者结合的相互促进作用；物资分配方案基于决策者偏好，并考虑每周期不同需求点的易损性、重要性、需求紧急性以及各类应急物资的重要性与时效性差异参数，有利于提高多周期决策的柔性和现实适用性。     

基于交叉效率DEA与群体共识的区间乘性语言偏好关系群决策

针对区间乘性语言偏好关系群决策问题，提出了一种基于交叉效率DEA和群体共识的群决策方法。首先，提出乘性语言偏好关系导出函数的定义，并构建产出导向的DEA模型，证明了一致性乘性语言偏好关系的DEA效率得分与排序向量之间存在比例关系。在此基础上，建立基于理想值的交叉效率DEA模型，提出乘性语言偏好关系的通用排序方法。同时，基于群体共识建立目标规划模型来计算各语言偏好关系的权重系数。最后，利用Monte Carlo随机模拟的方法对群体语言偏好空间进行统计分析，得到群决策期望排序向量及其可信度。算例分析表明本文方法能够有效的避免信息损失，具有较强的适用性和较高的可信度。     

A Model for the Transportation of Hazardous Waste*

Many decision problems, such as the transportation of hazardous waste, can be modeled by networks. However, due to the imprecise nature of much of the information decision makers have available, it is sometimes difficult to determine a best approach to the problem. To alleviate this problem, a network model that combines both precise and imprecise information is presented for the transportation of hazardous waste. The properties of the network model are investigated and solution procedures are presented.