评估项目风险中协调偏好的DEA方法

针对于综合考虑项目风险评估问题中影响风险因素的正向指标和逆向指标,在分析DEA有效解和多目标规划Pareto解等价性的基础上,建立了基于DEA的协调偏好的风险评估模型,优化协调了具有相反评价指标的项目风险评估问题。根据模型构造的生产可能集,结合两种相反评价指标对风险评估的影响,给出了风险包络面的概念,通过选取两组移动因子和具有熵权的模糊综合评价方法,逐层缩小风险包络面对决策单元进行评估排序,改进了移动风险曲面排序风险的方法,实现了风险决策单元的完全排序,使得评估结果在一定程度上兼具主观性与客观性。最后通过一个对软件项目进行风险评估的实际算例,表明了该方法的合理性和有效性。     

基于模糊判断矩阵的两阶段群决策方法

针对多属性群决策问题,提出一种两阶段决策分析方法。通过分析积型模糊一致性判断矩阵和模糊判断矩阵的排序向量之间的偏差,建立并求解一个规划模型,得到专家模糊判断矩阵的排序向量。由最小化专家模糊判断矩阵的排序向量与专家群组排序向量的偏差,再次建立并求解一个规划模型,得到反映专家群组偏好的排序向量,从而得出基于模糊判断矩阵的两阶段群决策方法。通过2个算例说明了该方法的可行性与有效性。     

改进TOPSIS的多时刻融合直觉模糊威胁评估

针对防空作战中的目标威胁评估问题,提出一种新的多时刻融合直觉模糊数排序模型.首先,根据目标威胁属性的主、客观权重得到综合权重;然后,通过逼近理想解排序法衡量直觉模糊数信息量的大小,利用直觉模糊熵表征直觉模糊数信息的可靠性,并结合决策者的风险偏好构建基于信息量和不确定性的直觉模糊数排序模型,得出单时刻的目标威胁排序;最后,利用泊松分布逆形式构建时间序列权重,从而融合多个时刻的决策信息,得出最终的目标威胁排序.仿真结果表明,所提出的算法综合了多时刻的决策信息,并可根据决策者的风险偏好进行调整,灵活性强、可靠性高.     

考虑偏好反转的区间不确定多属性决策方法

针对实际决策中的不确定性和偏好反转问题, 提出一种区间不确定多属性决策方法. 该方法先用证据推理方法集结区间不确定评估信息, 采用累积前景理论代替主观期望效用理论以构建方案的综合前景价值, 从而应对不确定环境下可能的决策偏好反转, 最后区间可能度用于方案综合前景价值排序. 介绍了决策过程, 给出求解方案综合前景价值的非线性规划模型, 并通过实例验证了方法的可行性、合理性和有效性.     

融合上下文信息与自注意力机制的群组推荐算法

针对群组推荐中组内成员存在偏好动态复杂性的问题,群组成员间的偏好会彼此影响,提出了一种融合上下信息与自注意力机制的群组推荐算法。首先,为了更好地捕捉群组成员间的动态关系,利用自注意力机制自动学习群组内用户的上下文信息与群组中其他成员间的关系权重;其次,根据自注意力机制学习到的组矩阵取其均值嵌入得到群组偏好表示;最后,根据群组偏好利用贝叶斯个性化排序方法进行群组推荐。将提出的方法在MovieLens-1M和CAMRa2011两个数据集上进行实验,并且与基准算法进行比较分析,该方法在推荐的命中率HR和NDCG两个指标上均有提升,实验结果证明该方法得到了更优的群组推荐结果。     

基于模糊树图与DS证据理论的机器人功能模块粒度划分方法

基于机器人技术中间件(RTM)提出一种功能模块粒度划分评价方法.首先,结合模糊层次分析法(FAHP)获取模块功能与结构相关性指标的综合权重,构造系统相关矩阵,通过模糊树图聚类算法得到不同粒度下的机器人系统模块划分方案;以模块独立性为原则,构建各模块划分方案的内聚度与耦合度模型,并将其视为DS(Dempster-Shafer)证据理论的两个证据源,建立多属性决策矩阵;通过区间偏好排序法对决策方案的信任区间进行排序,得到机器人系统最优模块划分方案.以机器人3维地图创建系统为例,对所提评价方法进行验证,系统实现及结果表明了该方法的有效性和可行性.     

基于决策单元异质性的群组交叉效率评价方法

交叉效率评价方法是数据包络分析（DEA）的拓展工具,但是现存交叉效率方法是对所有决策单元（DMUs）统一测评,没有考虑决策单元间的异质性问题。提出了一种考虑决策单元异质性的群组交叉效率模型,将具有异质性的群组间效率值进行合理集结的绩效评价方法。运用仁慈型交叉效率模型分析各群组内部的相对效率值;运用改进的熵权法为群组内部各决策单元分配适当权重,得到群组整体的最优权重向量;运用传统交叉效率模型评价与分析群组之间的相对效率值,并以此进行综合排序。为证明该方法具有理论与适用效力,2015年应用于16家中国商业银行的绩效评价,结果表明该方法行之有效。     

基于方案偏好和部分权重信息的模糊多属性决策方法

研究了只有部分权重信息且决策者对方案的偏好信息以三角模糊数互反判断矩阵形式给出的模糊多属性决策问题.首先为得到属性权重,给出一种结合主观模糊偏好信息和客观决策信息的极小化极大偏差模型;然后,运用加性加权法求出各方案的模糊综合属性值,并利用已有的三角模糊数排序公式求得决策方案的排序;最后,通过算例说明了该方法的可行性和有效性.     

信息安全风险控制的PROMETHEE决策方法研究

在成本效益分析的基础上,引入“级别高于关系”的PROMETHEE多属性偏好指数,提出了基于PROMETHEE方法的信息安全风险控制模型。该模型利用决策者给出的偏好,设置偏好函数、准则值和准则权重,从方案优劣程度入手,计算备选方案的“优势流”和“劣势流”,得到方案集的部分或完全排序,并基于此对安全控制措施的备选方案进行有效地筛选。还对该模型的灵敏度进行了分析和验证,最后结合实例分析了该风险控制模型的有效性。     

基于前景和量子理论的模糊多属性决策方法

针对属性权重不完全确定且属性偏好值为区间直觉模糊数的多属性决策问题,提出一种基于前景理论和量子进化算法的模糊多属性决策方法。该方法根据前景理论及模糊数距离公式,定义区间直觉模糊数的前景价值函数,同时将决策者对方案的风险偏好纳入决策行为中,以此来构建方案综合前景值最大化的非线性规划模型。通过引入量子进化算法,求解模型得出最优权重向量。最终根据方案前景值确定出方案的排序。该方法适用于模糊决策环境,能满足决策者不提供确定属性权重的要求,并充分考虑决策者风险心理因素对决策行为的影响,具有广泛的应用价值。数值算例说明了该方法的有效性和可行性。     

基于FAHP的信息安全风险评估方法

提出基于模糊层次分析法的信息安全风险综合评估模型,从主观评测和工具检测两方面对各个风险因素分别评价其重要程度。利用模糊偏好法求出各个风险因素在系统风险评估中的优先级排序,给出目标系统在不同安全侧面上的量化风险,增强评估准确性。实例分析表明,该模型可方便地应用于信息安全风险评估,具有实用性。     

A method for the evaluation of risk in IT projects

Information technology projects are particularly prone to failure due to their specific characteristics, making risk management become one of the critical elements in IT projects management. That is why several authors have developed risk evaluation methods, some of them based on fuzzy logic. This article proposes a new risk assessment method based in a combination of fuzzy analytic hierarchy process (FAHP) and fuzzy inference system (FIS). FIS is used for the integration of the groups of risk factors. These risk factors are the evaluation criteria of a modified FAHP which minimizes the disadvantages of the classic implementation of FAHP in order to obtain a more intuitive and easily adjustable model for multicriteria decision analysis with a lower computational need. The proposed model takes into consideration the different levels of uncertainty, the interrelationship among groups of risk factors, and the possibility of adding or suppressing options without losing the consistency with previous evaluations. The new method is especially suitable for the evaluation of development projects in the area of IT in which multiple interrelated risk factors can be particularly uncertain and imprecise. To implement the evaluation method, a hierarchy of risk factors was implemented. A numerical example is presented with data from three actual cases of IT projects, showing the applicability of the new method, the suitability of the selected taxonomy, and the significance of a few risk factors. Several future lines of work are proposed.     

Group Decision Making With Consistency of Intuitionistic Fuzzy Preference Relations Under Uncertainty

Intuitionistic fuzzy preference relation (IFPR) is a suitable technique to express fuzzy preference information by decision makers (DMs). This paper aims to provide a group decision making method where DMs use the IFPRs to indicate their preferences with uncertain weights. To begin with, a model to derive weight vectors of alternatives from IFPRs based on multiplicative consistency is presented. Specifically, for any IFPR, by minimizing its absolute deviation from the corresponding consistent IFPR, the weight vectors are generated. Secondly, a method to determine relative weights of DMs depending on preference information is developed. After that we prioritize alternatives based on the obtained weights considering the risk preference of DMs. Finally, this approach is applied to the problem of technical risks assessment of armored equipment to illustrate the applicability and superiority of the proposed method.      

基于方案修订的群决策一致性形成的研究

层次分析法(AHP)是群决策中经常使用的一种方法,利用AHP进行群决策的过程实质上也是决策者个体偏好集结的过程。针对如何将不同形式的偏好信息进行有效集结以形成群决策一致性方案的问题,提出一种通过活用AHP修订决策方案达到决策者群体的一致性偏好最终得以形成的方法,在利用决策者的决策信息进行群组划分的基础上明确各个划分的决策偏好差异,提出了活用AHP进行群决策一致性形成的方向,并形成了有效的动态群决策过程。     

Development of a decision support system for machining center selection

The machining centers are key resources for manufacturing companies in their dealing with their fierce competitive market environments. However, although selecting the most appropriate machining center is a very important decision for manufacturing companies, the availability of wide-range of types and models makes the selection process a complex and difficult task. In this study, a decision support system (DSS), namely MACSEL, is developed to help the decision makers in their machining center selection decisions. Several issues and applicability of the MACSEL is illustrated with case problems in the paper.Within the developed DSS, to select the feasible set of machining centers fifteen questions are placed in the elimination (pre-selection) module. The developed DSS uses fuzzy analytical hierarchy process (FAHP) or fuzzy technique for order preference by similarity to ideal solution (FTOPSIS), which are extended versions of multi-criteria decision making approaches, to rank the feasible machining centers. In the DSS, FAHP is used if a detailed pair-wise weighting of the hierarchically structured criteria is wanted. On the other hand, when a simpler separate weighting of each criterion is be considered as enough, FTOPSIS is used.     

概率语言术语集多准则决策方法研究进展

多准则决策理论与方法的应用能否达到预期的经济效益与社会效益,逐步成为该研究领域关注的核心议题。近年来,基于概率语言术语集（Probabilistic Language Term Set,PLTS）的决策理论与方法受到广泛关注,PLTS既能够反映决策者对方案或准则的犹豫模糊语言评价或比较偏好,又能体现各语言术语的概率信息,较为贴近决策者的思维认知过程,已广泛应用于投资项目风险评估、模式识别、医疗诊断等领域,产生了良好的应用效果。从PLTS的信息融合理论、测度理论、偏好关系理论、基于PLTS的多准则决策方法这四方面系统回顾PLTS的研究现状,分析现有研究存在的不足,指出采用概率不确定语言术语集（Probabilistic Uncertain Linguistic Term Set,PULTS）即能更为高效、精准地反映定性决策信息的模糊不确定性。阐述该领域后续研究应当重点关注PULTS的信息融合、测度理论框架、偏好关系的一致性-共识达成过程、多准则群决策方法及其应用研究等核心问题,并给出相应的研究思路,为该领域后续研究提供可行性借鉴。     

基于累积前景理论的大群体风险型动态应急决策方法

针对复杂环境下决策者对于应急事件作出的决策往往会面对偏好转移的问题,提出一种新的大群体风险型动态应急决策方法.首先利用偏好判断矩阵对全体决策者偏好进行聚类分析和偏好集结;其次,利用累积前景理论计算决策大群体的总体前景值;再次,考虑未来状态转移链,经过多轮调整得出决策者偏好转移矩阵,结合偏好转移矩阵和大群体总体前景值可得到当前突发事件状态下的最优方案;最后,通过案例分析与对比表明所提出方法的有效性和可行性.     

Preference solutions of probability decision making with rim quantifiers

This article extends the quantifier‐guided aggregation method to include probabilistic information. A general framework for the preference solution of decision making under an uncertainty problem is proposed, which can include decision making under ignorance and decision making under risk methods as special cases with some specific preference parameters. Almost all the properties, especially the monotonicity property, are kept in this general form. With the generating function representation of the Regular Increasing Monotone (RIM) quantifier, some properties of the RIM quantifier are discussed. A parameterized RIM quantifier to represent the valuation preference for probabilistic decision making is proposed. Then the risk attitude representation method is integrated in this quantifier‐guided probabilistic decision making model to make it a general form of decision making under uncertainty. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1253–1271, 2005.     

Evolutionary risk preference inference model using fuzzy support vector machine for road slope collapse prediction

Road slope collapse events are frequent occurrences in Taiwan, often exacerbated by earthquakes and/or heavy rainfall. Such collapses disrupt transportation, damage infrastructure and property, and may cause injuries and fatalities. While significant efforts are regularly invested in reducing road slope collapse risk, most focus exclusively on limiting the potential for slope failure. Collapse prediction efforts may result in inference errors that cause allocated road slope maintenance resources to be expended inefficiently, resulting in relatively higher collapse risk than should be achievable under ideal circumstances. Most maintenance programs rely on decision maker risk preferences, as his/her knowledge and experience can contribute to risk assessment decision making. The decision maker is capable of choosing an acceptable balance between two types of inference error, i.e., α and β errors. This preference may later be used as guidance to minimize inference error. This paper proposed the evolutionary risk preference fuzzy support vector machine inference model (ERP-FSIM) as a hybrid AI system able to make predictions regarding road slope collapse that takes decision maker risk preference into account. Validation results demonstrate ERP-FSIM viability, as level of average error both for the training set and validation set conform to the decision maker risk preference ratio and is significantly lower than the error tolerance of ±10%.     

Group decision making based on multiple types of linguistic preference relations

In this paper, we investigate group decision making problems with multiple types of linguistic preference relations. The paper has two parts with similar structures. In the first part, we transform the uncertain additive linguistic preference relations into the expected additive linguistic preference relations, and present a procedure for group decision making based on multiple types of additive linguistic preference relations. By using the deviation measures between additive linguistic preference relations, we give some straightforward formulas to determine the weights of decision makers, and propose a method to reach consensus among the individual preferences and the group’s opinion. In the second part, we extend the above results to group decision making based on multiple types of multiplicative linguistic preference relations, and finally, a practical example is given to illustrate the application of the results.