证据视角下考虑多参考点的直觉模糊多属性决策模型

针对直觉模糊多属性决策中,决策者内心同时存在多个独立参考点并且各属性之间相互关联的问题,进一步考虑智能传感设备在决策中的参考作用,提出证据视角下考虑多参考点的直觉模糊多属性决策模型。模型首先利用证据理论融合各传感器数据,得到各状态的mass函数;其次,考虑决策者内心同时存在多个参考点,利用价值函数得到各状态下多参考点价值矩阵;进一步,针对属性间的关联性,利用模糊积分得到各状态下不同方案的综合评价值;再次,利用基于证据理论的直觉模糊诱导有序加权平均(DS-IFIOWA)算子将各状态下不同方案的综合评价值进行集结,得到方案的总评价值,并以此对方案进行排序和优选。最后,利用数值算例验证了模型的有效性和可行性。     

考虑可信度和属性优先级的犹豫模糊决策方法

研究了考虑可信度的犹豫模糊混合集成因子以及考虑属性优先级的犹豫模糊多属性决策方法。首先给出了用于衡量数据差异程度的加权变异率公式,并证明了其具有类似于基尼系数的优良度量性质,之后在此基础上提出了可信度诱导犹豫模糊混合平均(CIHFHA)算子。针对属性权重信息未知的犹豫模糊决策问题,构建了一种新的考虑属性优先级的熵值修正G1的组合赋权方法,该方法可有效地利用属性客观评价数据以及通过考虑属性优先级体现专家意见,解决了主客观权重分配问题,得出的属性权重更加客观、合理。之后给出了一种基于CIHFHA算子和组合赋权方法的多属性决策方法,算例说明该方法的有效性和实用性。     

基于二元语义TAC积分算子的语言型多属性群决策方法

针对属性之间存在模糊关联的语言型多属性群决策问题,给出了二元语义TAC(Two-Additive Choquet)积分算子的定义,分析和证明了算子的有关性质,并提出了相应的决策方法。该方法首先将各专家提供的语言短语形式的属性权重信息、属性关联信息与属性评价信息转化为二元语义形式,然后利用二元语义TAC积分算子将转化后的属性相关信息集结为各专家的方案评价值,并进一步集结专家意见获得方案的综合评价值,从而确定其排序。最后,通过实例分析和方法比较说明了所给方法的有效性和优点。研究结果表明,该方法具有属性关联刻画细致、计算过程简单且无信息损失、决策结果可解释性强等优点,为求解属性之间存在模糊关联的语言型多属性群决策问题提供了一种新的途径。     

基于属性间交互作用指标的多属性决策方法

多属性决策问题的各属性之间通常存在关联,而这种关联对决策的影响不可以忽略。基于属性间的交互作用指标,本文提出了一种新的评价函数,并研究了该评价函数的性质。首先,本文详细介绍了合作对策中几个常用的交互作用指标的概念。然后,提出了基于属性间交互作用指标的交互加权评价函数,简称为IWE函数。同时,本文给出了IWE函数的相关性质,并讨论了IWE函数与Choquet积分算子的联系与区别。最后,以算例验证本文所提方法在多属性决策中的合理性与有效性。     

基于区间型多属性决策的突发事件属性熵权的确定

突发事件的实时数据是应急决策的依据,提高对数据的处理能力,确定突发事件属性的熵权,筛选出反映事件发展趋势的重要属性,是提高决策效率和准确性的关键问题。利用共性知识模型结构化表示突发事件和属性,参考区间型多属性决策方法,把事件属性监测值转换为区间数型,在保持时序信息的同时降低数据维数,并通过定义精确数与区间的距离,使突发事件属性集均转化为成本型属性;继而利用基于熵权的区间型多属性决策方法计算事件的属性熵权值,权值越大,表示包含事件演化趋势的信息越多,在决策时就应被重点关注。最后,通过实例说明此方法的有效性和实用性。     

Fuzzy optimality based decision making under imperfect information without utility

In the realm of decision making under uncertainty, the general approach is the use of the utility theories. The main disadvantage of this approach is that it is based on an evaluation of a vector-valued alternative by means of a scalar-valued quantity. This transformation is counterintuitive and leads to loss of information. The latter is related to restrictive assumptions on preferences underlying utility models like independence, completeness, transitivity etc. Relaxation of these assumptions results into more adequate but less tractable models. In contrast, humans conduct direct comparison of alternatives as vectors of attributes’ values and don’t use artificial scalar values. Although vector-valued utility function-based methods exist, a fundamental axiomatic theory is absent and the problem of a direct comparison of vectors remains a challenge with a wide scope of research and applications. In the realm of multicriteria decision making there exist approaches like TOPSIS and AHP to various extent utilizing components-wise comparison of vectors. Basic principle of such comparison is the Pareto optimality which is based on a counterintuitive assumption that all alternatives within a Pareto optimal set are considered equally optimal. The above mentioned mandates necessity to develop new decision approaches based on direct comparison of vector-valued alternatives. In this paper we suggest a fuzzy Pareto optimality (FPO) based approach to decision making with fuzzy probabilities representing linguistic decision-relevant information. We use FPO concept to differentiate “more optimal” solutions from “less optimal” solutions. This is intuitive, especially when dealing with imperfect information. An example is solved to show the validity of the suggested ideas.     

不同直觉偏好信息下的多属性决策方法

研究了属性值为实数且决策者对属性的偏好信息以直觉判断矩阵或残缺直觉判断矩阵给出的模糊多属性决策问题.首先介绍了直觉判断矩阵、一致性直觉判断矩阵、残缺直觉判断矩阵、一致性残缺直觉判断矩阵等概念,而后分别考虑关于直觉判断矩阵和残缺直觉判断矩阵的多属性决策问题,接着建立了基于直觉判断矩阵和残缺直觉判断矩阵的多属性群决策模型,通过求解这些模型获得属性的权重.进而给出了不同直觉偏好信息下的多属性决策方法.最后通过一个例子说明了该方法的可行性和实用性.     

离差最大化时基于交叉评价的多属性决策方法

针对属性权重完全未知,且评价系统内既有客观数据、又有主观因素的多属性决策问题,提出一种基于离差最大化和交叉评价的模糊多属性决策方法。该方法首先定义了最小交叉效率、最大交叉效率等概念,将量化指标用交叉评价方法进行处理;然后采用模糊综合评价方法评价非量化指标,并基于离差最大化思想确定权重;最后将模糊化处理之后的量化指标与非量化指标一起进行最终评价,建立离差最大化条件下基于交叉评价的模糊多属性决策模型。最后通过算例验证了方法的可行性及有效性。     

Group decision making with simplified neutrosophic ordered weighted distance operator

Simplified neutrosophic set is a convenient tool proposed for dealing with complex problems; it is effective in providing more data for decision‐making process. In this study, we develop a simplified neutrosophic ordered weighted distance operator which combines the neutrosophic distance measures and the ordered weighted average distance in the same formulation. It is a new handy aggregation operator that considers the situations where the input data are represented in simplified neutrosophic numbers, and it also contains diverse distance aggregation operators. Parameterized families of simplified neutrosophic ordered weighted distance operator are handled. Moreover, we establish a new neutrosophic group decision‐making method based on the simplified neutrosophic ordered weighted distance operator, which has 2 extended approaches for determining the weights of decision makers and decision attributes in decision‐making process, respectively. Finally, an illustrative example demonstrates the application of the proposed method. The effectiveness and advantages of the proposed method are shown by the comparative analysis with existing relative methods.     

Prioritization of e-learning forms: a multicriteria methodology

Strategic planning of e-learning implementation includes decision making about the most suitable form of implementing e-learning on different levels in an institution. Decision making about e-learning implementation has been covered as consisting of four phases: (1) intelligence, (2) design, (3) choice and (4) implementation. During the Intelligence phase we have precisely identified our central decision problem and have conducted situation analysis. In the Design phase we have developed alternatives and established criteria and subcriteria. The questionnaire about the importance of the advantages and goals of e-learning implementation and about criteria and subcriteria significant for decision making was created. Essential for the survey was use of Croatian e-learning experts that are familiar with higher education (HE) environment. Further, we connected these findings with the results of the factor analysis which was performed on the survey. The results of the factor analysis have served as input in the multicriteria decision model (AHP) that we have developed in the Choice phase. In the implementation phase we have solved the problem of prioritisation of e-learning options with the help of multi-criteria modelling in the process of group decision making. In this article, firstly we will present and analyze the results of the survey. Secondly, the outputs of factor analysis will be stated and compared with the model used in the questionnaire. Finally, the structure of AHP model will be given and the results of the quantitative evaluation of the model will be presented.     

Rank order data in DEA: A general framework

In data envelopment analysis (DEA), performance evaluation is generally assumed to be based upon a set of quantitative data. In many real world settings, however, it is essential to take into account the presence of qualitative factors when evaluating the performance of decision making units (DMUs). Very often rankings are provided from best to worst relative to particular attributes. Such rank positions might better be presented in an ordinal, rather than numerical sense. The paper develops a general frame work for modeling and treating qualitative data in DEA and provides a unified structure for embedding rank order data into the DEA framework. The existing techniques are discussed and their equivalence is demonstrated. Both continuous and discrete projection models are provided. It is shown that qualitative data can be treated in conventional DEA methodology.     

A discrete method based on distance measure in linguistic space and its application for CMADM problem

ABSTRACT

Owing to the complexity of decision environment, not all the attributes in multiple attribute decision making are quantitative. There are also some qualitative attributes, which are related to the integration of multiple attribute decision making (MADM) and linguistic multiple attribute decision making (LMADM). The specific method for composite multiple attribute decision making (CMADM) problems is crucial for decision maker (DM) to make scientific decision. In this paper, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is extended to a Composite Technique for Order Preference by Similarity to an Ideal Solution (CTOPSIS) method to solve the CMADM problems. As the basis of the CTOPSIS method, the distance measure model in linguistic space and in n-dimension linguistic space is generated based on the non-linear mapping. Based on the distance measure in linguistic space, a standard deviation method is taken to get the attribute weight. At the same time, the distance measure models are proposed based on the distance measure in n-dimension linguistic space, which are used to calculate the distance between the alternatives and the positive and negative idea points separately. Furthermore, a CTOPSIS method is generated to solve the CMADM problems. Finally, a numerical example is illustrated to explain the process. And the result shows that the CTOPSIS method is quite practical and more approximate to the real decision making situation.     

Developing a straightforward approach for group decision making based on determining weights of decision makers

Group decision making is an active area of research within multiple attribute decision making. This paper assumes that all the decision makers (DMs) are not equally qualified to contribute equitably to the decision process. The aim of this paper is to develop an approach to determine weights of DMs, in which the decision information on alternatives with respect to attributes, provided by each DM, is represented in the form of interval data. We define the average of all individual decisions as the positive ideal decision (PID), and the maximum separation from PID as the negative ideal decision, which are characterized by a matrix, respectively. The weight of each DM is determined according to the Euclidean distances between the individual decision and ideal decisions. By using the obtained weights of DMs, all individual decisions are aggregated into a collective decision. Then the alternatives is ranked based on the collective decision. Meanwhile, this paper also gives a humanized decision method by using an optimistic coefficient, which is used in adjusting the relative importance between profit and risk. Finally, we give an example to illustrate the developed approach.     

基于QUALIFLEX和LINMAP的简化中性犹豫模糊多属性决策方法

在处理多属性决策问题中,QUALIFLEX是一种非常有用的排序算法。针对属性取值为简化中性犹豫模糊集的多属性决策问题,提出了SNHFS-QUALIFLEX算法。另外考虑到属性权重不确定的情况,将LINMAP扩展到简化中性犹豫模糊集中,定义了符号距离,建立了最优数学规划模型来确定属性权重。最后将SNHFS-QUALIFLEX方法应用到多属性决策实例中,并验证了其可行性和有效性。     

群体多指标决策的偏好集结方法

研究多个指标条件下,利用个体决策结果形成群体一致偏好的方法、假设个体有加性效用函数,将个体多指标效用函数表示成单个指标评价函数的加权和,群体指标评价函数表示成个体指标评价函数的加权和.通过协商指标权重、指标评价函数、支付意愿三个参数,成对个体达成双方一致.提出了(n-1)对个体之间达成双方一致,从而得出群体效用函数的决策方法,这种分析框架同样可以扩展到联盟协商一致中.     

Hybrid Multi-attribute Decision Making Method of Electric Coal Procurement in Industry

Electric coal procurement is the basis of electric power production. In this paper, the problem of supplier selection is studied in multi-source procurement of electric coal. Concretely, the index system of supplier selection is presented, including the evaluation attributes of price, quantity, quality, delivery time and the reputation of supplier. Then, the problem of supplier selection is converted into a problem of hybrid multi-attribute decision making, and a projection method based on hybrid technique for order preference by similarity to ideal solution (TOPSIS) is presented to rank all suppliers and select winners. Its decision example is also given to implement the presented decision method and to demonstrate its effectiveness and practicality. This paper gives an effective way to the hybrid multi-attribute decision making for multi-source procurement of electric coal under fuzzy uncertain environment.     

基于双边犹豫模糊非均衡语言优先集成算子的应急响应预案评估方法

在应用多属性决策理论求解应急响应预案评估问题时,问题结构的复杂性往往使决策者评价信息存在高度不确定性且属性相对重要性仅能以优先级关系来表征。为此,本文首先提出了双边犹豫模糊非均衡语言集这种新型信息形式以使决策者能够灵活有效的表征复杂评价信息,并定义了运算法则、熵和距离测度;其次,基于熵测度开发了双边犹豫模糊非均衡语言优先加权集成算子,并构建了能够考虑属性优先关系的多属性决策方法;进一步针对属性相对重要性不能由定性分析获得的情况,设计了客观权重确定方法,并构建了另一种更具实际灵活性的VIKOR决策方法;最后,实例研究表明了方法的有效性与优势。     

基于多Agent的车间任务招投标粗模糊集建模研究

首先通过相识集、招标集、投标集和任务集的概念 ,描述制造执行系统中的调度 Agent与资源 A-gent间任务招投标过程模型 ;基于任务的属性和资源 Agent完成任务的成本、质量、负荷和时间等属性 ,定义论域上的模糊集 ,将模糊集中的隶属度函数作为粗集的属性 ,在模糊集上作截集 ,从而获得系统的分类知识 ;收集样本数据 ,构造并分析决策表 ,进而获得调度 Agent调度决策知识 ;应用调度知识进行推理 ,从争取获得招标任务的若干个资源 Agent中 ,选出最适合招标任务的中标者 .     

基于灰区间数决策的城市土地集约利用评价

城市土地集约利用评判是典型的区间数多属性决策问题,将基于灰色系统理论的多指标灰区间数关联决策模型应用于城市土地利用状况评价领域,通过引入灰区间数序列的范数完成多指标区间数决策矩阵的规范化处理,建立了多指标区间数整体逼近决策方法,使城市土地集约利用评判方法更加客观.最后通过扬州市城市土地利用状况为实例验证模型的有效性和实用性,为城市土地集约利用评价提供了一种新方法.