直觉模糊序决策信息系统的分布约简

本文在直觉模糊决策信息系统中引入直觉模糊序关系,建立了基于直觉模糊序关系的决策信息系统.然后,基于定义的分布协调集,给出相应分布约简的判定定理和可辨识分布矩阵,从而提供了直觉模糊序决策信息系统的属性约简方法.最后通过例题验证方法的有效性.     

基于级别优序关系和层次分析法的铝合金焊接方法决策

焊接工作者的经验总是有限的,凭经验选出的焊接方法不能保证最合适,因此开发出一种多属性决策专家系统是当前焊接工作者所急需的.在分析各种多属性决策方法的优缺点基础上,提出了基于级别优序关系和层次分析法的铝合金焊接方法决策算法.采用对数模糊优先规划方法从模糊判断矩阵中得出精确权重值,采用基于级别优序关系中的偏好顺序结构评估法(PROMETHEE)对可行方案进行排序,通过一个具体实例阐述了决策流程,结果表明方法具有一定的实用价值.最后指出研究中的不足和进一步研究方向.     

风险厌恶条件下竞争报童问题的二层规划模型

有效应用数学规划方法研究报童类产品的库存管理,能够降低企业的成本和风险,从而提高企业的经济效益.此类问题的研究,目前主要集中在应用Nash博弈研究竞争报童问题以及零售商所持风险态度对供应链库存水平的影响.试图研究序贯决策下的竞争报童问题模型以及零售商所持风险态度对供应链库存水平的影响.由此建立了二层规划模型.算例表明序贯决策的竞争报童问题中,总订购量随风险厌恶程度的提高而降低.     

NBU(2)性的偏序刻画

为刻画同一寿命分布类中两个不同分布的NBU(2)性的强弱,本文定义了一个新的偏序,即NBU(2)序,并证明NBU(2)序具有反身性,反对称性和传递性.另外,该偏序与超可加序、NBUE序,增凹序的关系也作了探讨.基于被比较分布是指数的情况,还建立了判定NBU(2)序的一个充要条件.     

不确定多属性决策中区间数排序的一种新方法

根据格序决策理论,提出不确定多属性决策中区间数排序的新方法,将综合评价值区间数分为可比较与不可比较区间数,对可比较区间数利用区间数上界进行比较来排序,而对不可比较区间数利用均值及区间上界比较来排序,方法比格序决策法和期望-方差法等方法更加简单易行,进一步提高了决策效率.最后通过算例验证方法的可行性和有效性.     

关于Rees商超序半群

本文给出了Rees同余商超序半群的详细构造,将序半群上的Rees同余商半群推广到超序半群上.本文的主要结论是:设(S,.,≤)为超序半群,K是S的理想,记S | K:=(S\K)U{0},其中0是K中任一元素,那么存在S|K上的二元超运算*以及偏序关系(<)使得(S|K,*,(<))是一个超序半群.     

关于序超半群的左超理想的研究

本文在序超半群中引入了极小左超理想和极大左超理想的概念, 并讨论了它们的一些相关性质. 进一步地, 引入了序超半群的弱素左超理想、拟素左超理想、拟半素左超理想及弱拟素左超理想的概念, 并讨论这四种素超理想之间的关系. 而且通过左超理想和弱~$m$-系刻画了序超半群的弱拟素左超理想. 同时, 借助于$m$-系对序超半群的拟素左超理想给出刻画. 尤其证明了序超半群$S$是强半单的当且仅当$S$的每个左超理想是$S$的包含它的所有拟素左超理想的交.     

协调区间值决策形式背景的知识约简

知识约简是概念格理论的核心问题之一.主要讨论协调区间值决策形式背景的知识约简问题.首先从经典的协调决策形式背景出发,定义了协调区间值决策形式背景,同时给出了协调区间值决策形式背景上决策保序集的定义和判定定理,并进一步阐明了决策保序集和协调集之间的关系,然后通过定义辨识矩阵,给出了协调区间值决策形式背景的属性约简方法.     

编序式问卷调查的模糊偏序排序法

讨论了现有的编序式问卷调查排序方法的不足之处;提出了基于模糊偏序关系的编序式问卷调查的排序方法,给出了模糊偏序排序法的数学模型,论证了用模糊偏序关系对编序式问卷调查进行排序是一种较好的方法;并用实例分析了模糊偏序排序法应用于编序式问卷调查的具体操作步骤.     

不完备序区间值决策系统中的可信规则获取

以不完备序区间值决策系统为研究对象,其中不仅包含遗漏型未知区间值,而且属性值域为全序集.给出了未知区间值的三种形式及其填充式区间值的定义,引入灰的白化方法用以构建一个新的填充式不完备序白化值决策系统,并讨论其在优势和弱势关系下的可信规则获取.进一步研究了优势和弱势对象的约简以及其决策类的相对约简问题,给出了相应的判定定理与区分函数,为最终从不完备序区间值决策系统中获取最优可信决策规则提供了新的理论基础与操作手段.、     

Multicriteria sorting using a valued indifference relation under a preference disaggregation paradigm

Most multicriteria decision methods need the definition of a significant amount of preferential information from a decision agent. The preference disaggregation analysis paradigm infers the model’s parameter values from holistic judgments provided by a decision agent. Here, a new method for inferring the parameters of a fuzzy outranking model for multicriteria sorting is proposed. This approach allows us to use most of the preferential information contained in a reference set. The central idea is to characterize the quality of the model by measuring discrepancies and concordances amongst (i) the preference relations derived from the outranking model, and (ii) the preferential information contained in the reference set. The model’s parameters are inferred from a multiobjective optimization problem, according to some additional preferential information from a decision agent. Once the model has been fitted, sorting decisions about new objects are performed by using a fuzzy indifference relation. This proposal performs very well in some examples.     

Extensions of the prudence principle to exploit a valued outranking relation

In many outranking methods, the alternatives are first compared pairwise to build a valued outranking relation, which is then exploited to derive a recommendation for the decision maker. This paper proposes extensions of Arrow and Raynaud’s prudence principle to exploit a valued outranking relation, even in cases where the outranking values have an ordinal interpretation. The idea is to build a ranking that maximizes the weakest support for its implicit pairwise comparisons. We present a family of exploitation models providing three types of solutions, depending on the decision maker’s request: a linear order, a weak order, or a partial preorder. An algorithm is provided which solves the exploitation models. Relations between these models and some of their properties are also discussed.     

Expert knowledge and computer-aided group decision making: Some pragmatic reflections

Based on a simple outranking method of multi-attribute decision making (MADM), called Dichotomy-Cut, developed in recent years and published elsewhere, this paper describes how to reliably elicit the necessary decision knowledge for implementing various decision modes via this method and its computerized support - UNIDAS 2. The most important features of this method are outlined, namely, how to compose the preference ordered matrices and which are the basic decision rules. Special attention should be paid to specific interactive group decision behaviour of experts. Two examples plainly explain the contents discussed.     

Outranking approaches versus MAUT in MCDM

The authors summarize the Outranking and MAUT (Multiple Attribute Utility Theory) methodologies and indicate the existence of a bibliography on outranking methods and their applications in multiple criteria decision making.     

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。     

ELECTRE: Robust ordinal regression for outranking methods

We present a new method, called ELECTRE^GKMS, which employs robust ordinal regression to construct a set of outranking models compatible with preference information. The preference information supplied by the decision maker (DM) is composed of pairwise comparisons stating the truth or falsity of the outranking relation for some real or fictitious reference alternatives. Moreover, the DM specifies some ranges of variation of comparison thresholds on considered pseudo-criteria. Using robust ordinal regression, the method builds a set of values of concordance indices, concordance thresholds, indifference, preference, and veto thresholds, for which all specified pairwise comparisons can be restored. Such sets are called compatible outranking models. Using these models, two outranking relations are defined, necessary and possible. Whether for an ordered pair of alternatives there is necessary or possible outranking depends on the truth of outranking relation for all or at least one compatible model, respectively. Distinguishing the most certain recommendation worked out by the necessary outranking, and a possible recommendation worked out by the possible outranking, ELECTRE^GKMS answers questions of robustness concern. The method is intended to be used interactively with incremental specification of pairwise comparisons, possibly with decreasing confidence levels. In this way, the necessary and possible outranking relations can be, respectively, enriched or impoverished with the growth of the number of pairwise comparisons. Furthermore, the method is able to identify troublesome pieces of preference information which are responsible for incompatibility. The necessary and possible outranking relations are to be exploited as usual outranking relations to work out recommendation in choice or ranking problems. The introduced approach is illustrated by a didactic example showing how ELECTRE^GKMS can support real-world decision problems.     

Interactive outranking approaches for multicriteria decision-making problems with imprecise information

PROMETHEE is a powerful method, which can solve many multiple criteria decision making (MCDM) problems. It involves sophisticated preference modelling techniques but requires too much a priori precise information about parameter values (such as criterion weights and thresholds). In this paper, we consider a MCDM problem where alternatives are evaluated on several conflicting criteria, and the criterion weights and/or thresholds are imprecise or unknown to the decision maker (DM). We build robust outranking relations among the alternatives in order to help the DM to rank the alternatives and select the best alternative. We propose interactive approaches based on PROMETHEE method. We develop a decision aid tool called INTOUR, which implements the developed approaches.     

Weighted Overlap Dominance – A procedure for interactive selection on multidimensional interval data

We present an outranking procedure that supports selection of alternatives represented by multiple attributes with interval valued data. The procedure is interactive in the sense that the decision maker directs the search for preferred alternatives by providing weights of the different attributes as well as parameters related to risk attitude and weighted dominance. The outranking relation builds on pairwise comparisons between optimistic and pessimistic weighted values as well as weighted dominance relations supported by volume based measures. The suggested procedure is referred to as the Weighted Overlap Dominance procedure (WOD).     

<Emphasis Type="Bold">R</Emphasis><Emphasis Type="SmallCaps">UBIS</Emphasis>: a bipolar-valued outranking method for the choice problem

The main concern of this article is to present the R UBIS method for tackling the choice problem in the context of multiple criteria decision aiding. Its genuine purpose is to help a decision maker to determine a single best decision alternative. Methodologically we focus on pairwise comparisons of these alternatives which lead to the concept of bipolar-valued outranking digraph. The work is centred around a set of five pragmatic principles which are required in the context of a progressive decision aiding methodology. Their thorough study and implementation in the outranking digraph lead us to define a choice recommendation as an extension of the classical digraph kernel concept.      

Decision-aid for discrete multiple criteria decision making problems with imprecise data

We describe ways of aiding decision making with a discrete set of alternatives. In many decision situations, it is not possible to obtain explicit preference information from the decision makers. Instead, useful decision-aid can be provided to the decision makers by describing what kind of weighting of the criteria result in certain choices of the alternatives. The suggested treatment is based on the basic ideas of the ELECTRE III method. The modelling of the preferences by pseudo-criteria is especially helpful in case the data, that is, the criterion values are imprecise. Unlike ELECTRE III, no ranking of the alternatives is produced. Based on a minimum-procedure in the exploitation of the outranking relations, we provide information about the weights of the criteria that make a certain alternative the best. We also present an interactive searching procedure in the weighting space. The auxiliary optimization problems to be solved are nondifferentiable. Cases with both single and multiple decision makers are considered.