Multi-attribute decision making with generalized fuzzy numbers

This paper proposes a multi-attribute decision-making method with generalized fuzzy numbers (GFNs). In the proposed method, the distance between GFNs is calculated using the Hausdorff distance. Based on the maximizing deviation degree, the attribute weights are determined by a linear programming model. Furthermore, a ranking formula with a modified possibility degree is adopted to rank alternatives. A numerical example is introduced to validate the proposed model, and the results indicate that the proposed model offers a practical and effective way to meet the different assessment requirements of decision makers.     

Aggregation of fuzzy preference relations to multicriteria decision making

Weighted aggregation of fuzzy preference relations on the set of alternatives by several criteria in decision-making problems is considered. Pairwise comparisons with respect to importance of the criteria are given in fuzzy preference relation as well. The aggregation procedure uses the composition between each two relations of the alternatives. The membership function of the newly constructed fuzzy preference relation includes t-norms and t-conorms to take into account the relation between the criteria importance. Properties of the composition and new relation, giving a possibility to make a consistent choice or to rank the alternatives, are proved. An illustrative numerical study and comparative examples are presented.     

Duality for a class of fuzzy nonlinear optimization problem under generalized convexity

In this paper, a Mond-Weir type dual program for a nonlinear primal problem under fuzzy environment is formulated. The solution concept of primal-dual problems is inspired by the nondominated solution. We have considered ordering among fuzzy numbers as a partial ordering and using the concept of Hukuhara difference between two fuzzy numbers and $H$ -differentiability, appropriate duality theorems are established under pseudo/quasi-convexity assumptions. We have also illustrated a numerical example which satisfies the duality relations discussed in the paper.     

Vector optimization problem and generalized convexity

Some properties of α-weakly preinvex and pseudoinvex functions via Clarke-Rockafellar and limiting subdifferentials are obtained. Furthermore, the equivalence between vector variational-like inequalities and vector optimization problems are studied under pseudoinvexity condition.     

Discrete optimization algorithms and problems of decision making in a fuzzy environment

An approach to solving optimization problems with fuzzy coefficients is described. It consists in formulating and analyzing one and the same problem within the framework of mutually related models by constructing equivalent analogs with fuzzy coefficients in objective functions alone. Since the approach is applied within the context of fuzzy discrete optimization problems, modified algorithms of discrete optimization are discussed. These algorithms are based on a combination of formal and heuristic procedures and allow one to obtain quasi-optimal solutions after a small number of steps, thus overcoming the computational complexity posed by the NP-completeness of discrete optimization problems. The subsequent contraction of the decision uncertainty regions is associated with reduction of the problem to multiobjective decision making in a fuzzy environment using techniques based on fuzzy preference relations. The results of the paper are of a universal character and are already being used to solve practical problems in several fields.     

Convexifactors,generalized convexity and vector optimization

In this article we study a recently introduced notion of non-smooth analysis, namely convexifactors. We study some properties of the convexifactors and introduce two new chain rules. A new notion of non-smooth pseudoconvex function is introduced and its properties are studied in terms of convexifactors. We also present some optimality conditions for vector minimization in terms of convexifactors.     

Parameter convexity and concavity of generalized trigonometric functions

We study the convexity properties of the generalized trigonometric functions viewed as functions of the parameter. We show that p→_sinp?(y)$p\to {\sin }_p?(y)$ and p→_cosp?(y)$p\to {\cos }_p?(y)$ are log-concave on the appropriate intervals while p→_tanp?(y)$p\to {\tan }_p?(y)$ is log-convex. We also prove similar facts about the generalized hyperbolic functions. In particular, our results settle a major part of the conjecture recently put forward in [4].     

Aggregation and decision making using ranked data

Nonparametric procedures are frequently used to rank order alternatives. Often, information from several data sets must be aggregated to derive an overall ranking. When using nonparametric procedures, Simpson-like paradoxes can occur in which the conclusion drawn from the aggregate ranked data set seems contradictory to the conclusions drawn from the individual data sets. Extending previous results found in the literature for the Kruskal–Wallis test, this paper presents a strict condition that ranked data must satisfy in order to avoid this type of inconsistency when using nonparametric pairwise procedures or Bhapkar’s V procedure to extract an overall ranking. Aggregating ranked data poses further difficulties because there exist numerous ways to combine ranked data sets. This paper illustrates these difficulties and derives an upper bound for the number of possible ways that two ranked data sets can be combined.     

Ranking generalized fuzzy numbers in fuzzy decision making based on the left and right transfer coefficients and areas

Although a number of recent studies have proposed ranking fuzzy numbers based on the deviation degree, most of them have exhibited several shortcomings associated with non-discriminative and counter-intuitive problems. In fact, none of the existing deviation degree methods has guaranteed consistencies between the ranking of fuzzy numbers and that of their images under all situations. They have also ignored decision maker’s attitude toward risk, which significantly influences final ranking result. To overcome the above-mentioned drawbacks, this study proposes a new approach for ranking fuzzy numbers that ensures full consideration for all information of fuzzy numbers. Accordingly, an overall ranking index is obtained by the integration of the information from the left and the right (LR) areas between fuzzy numbers, the centroid points of fuzzy numbers and the decision maker’s attitude toward risk. This new method is efficient for evaluating generalized fuzzy numbers and distinguishing symmetric fuzzy numbers. It also overcomes the shortcomings of the existing approaches based on deviation degree. Several numerical examples are provided to illustrate the superiority of the proposed approach. Lastly, a new fuzzy MCDM approach for generalized fuzzy numbers is proposed based on the proposed ranking approach and the concept of generalized fuzzy numbers. The proposed fuzzy MCDM approach does not require the normalization process and thus avoids the loss of information results from transforming generalized fuzzy numbers to normal form.     

Hesitant fuzzy information aggregation in decision making

As a generalization of fuzzy set, hesitant fuzzy set is a very useful tool in situations where there are some difficulties in determining the membership of an element to a set caused by a doubt between a few different values. The aim of this paper is to develop a series of aggregation operators for hesitant fuzzy information. We first discuss the relationship between intutionistic fuzzy set and hesitant fuzzy set, based on which we develop some operations and aggregation operators for hesitant fuzzy elements. The correlations among the aggregation operators are further discussed. Finally, we give their application in solving decision making problems.     

A fuzzy closeness approach to fuzzy multi-attribute decision making

The aim of this paper is to develop a new fuzzy closeness (FC) methodology for multi-attribute decision making (MADM) in fuzzy environments, which is an important research field in decision science and operations research. The TOPSIS method based on an aggregating function representing “closeness to the ideal solution” is one of the well-known MADM methods. However, while the highest ranked alternative by the TOPSIS method is the best in terms of its ranking index, this does not mean that it is always the closest to the ideal solution. Furthermore, the TOPSIS method presumes crisp data while fuzziness is inherent in decision data and decision making processes, so that fuzzy ratings using linguistic variables are better suited for assessing decision alternatives. In this paper, a new FC method for MADM under fuzzy environments is developed by introducing a multi-attribute ranking index based on the particular measure of closeness to the ideal solution, which is developed from the fuzzy weighted Minkowski distance used as an aggregating function in a compromise programming method. The FC method of compromise ranking determines a compromise solution, providing a maximum “group utility” for the “majority” and a minimum individual regret for the “opponent”. A real example of a personnel selection problem is examined to demonstrate the implementation process of the method proposed in this paper.     

A fuzzy outranking relation in multicriteria decision making

It frequently happens that a decision maker must establish a ranking within a finite set of alternatives with respect to multiple criteria. The subjective evaluation of each alternative according to each criterion is expressed in the form of a distributive evaluation. To capture the preferences of one alternative over another, a concept of fuzzy outranking relation can be used. This fuzzy outranking relation is characterized by a degree of credibility which is computed from two indices: a confidence index and a doubt index. Each of these indices is calculated from the distributive evaluations over the various criteria. In this paper, such a fuzzy outranking relation (fuzzy binary relation) is constructed and an application is presented.     

Efficient and weak efficient points in vector optimization with generalized cone convexity

In this paper, we present necessary and sufficient conditions of efficiency and weak efficiency under generalized cone-convexity and cone-subconvexity. The results are stated in partially ordered real linear spaces from a separation theorem between convex cones which need not be solid.     

Type-2 fuzzy numbers made simple in decision making

Fuzzy Optimization and Decision Making - For the decision-making problems based on decision makers’ judgments in terms of linguistic terms, we propose type-2 fuzzy numbers (T2FNs) that allow...     

λ型传递矩阵与多因素模糊决策

基于已知常用的模糊矩阵传递性概念定义了λ型传递。给出了它的几个等价条件。研究它的图论特征，指出λ型传递矩阵的圈都过强对边二元圈。随后证明了与全传递模糊矩阵的等价性。进一步研究与截矩阵的性质一致问题，证明了λ型传递满足一致性。最后给出λ型传递在模糊排充中的应用，表明它是一种新的实用的多因素模糊决策的数学模型。     

Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems

Attribute reduction is viewed as an important issue in data mining and knowledge representation. This paper studies attribute reduction in fuzzy decision systems based on generalized fuzzy evidence theory. The definitions of several kinds of attribute reducts are introduced. The relationships among these reducts are then investigated. In a fuzzy decision system, it is proved that the concepts of fuzzy positive region reduct, lower approximation reduct and generalized fuzzy belief reduct are all equivalent, the concepts of fuzzy upper approximation reduct and generalized fuzzy plausibility reduct are equivalent, and a generalized fuzzy plausibility consistent set must be a generalized fuzzy belief consistent set. In a consistent fuzzy decision system, an attribute set is a generalized fuzzy belief reduct if and only if it is a generalized fuzzy plausibility reduct. But in an inconsistent fuzzy decision system, a generalized fuzzy belief reduct is not a generalized fuzzy plausibility reduct in general.     

Group decision making with a fuzzy linguistic majority

The determination of solutions in group decision making is considered. The point of departure is a collection of individual fuzzy preference relations. A solution is derived either directly from the individual fuzzy preference relations or by constructing first a social fuzzy preference relation. As opposed to conventional approaches in which a crisp (threshold type) majority rule is used, we employ a fuzzy majority rule specified by a fuzzy linguistic quantifier, e.g., ‘most’, ‘much more than 50%’, etc. A calculus of linguistically quantified propositions is applied. Using the fuzzy majority, various solution concepts are derived, mainly of the type of core, minimax (opposition) set and consensus winner.     

Multi-criteria decision making with fuzzy linguistic preference relations

Although the analytic hierarchy process (AHP) and the extent analysis method (EAM) of fuzzy AHP are extensively adopted in diverse fields, inconsistency increases as hierarchies of criteria or alternatives increase because AHP and EAM require rather complicated pairwise comparisons amongst elements (attributes or alternatives). Additionally, decision makers normally find that assigning linguistic variables to judgments is simpler and more intuitive than to fixed value judgments. Hence, Wang and Chen proposed fuzzy linguistic preference relations (Fuzzy LinPreRa) to address the above problem. This study adopts Fuzzy LinPreRa to re-examine three numerical examples. The re-examination is intended to compare our results with those obtained in earlier works and to demonstrate the advantages of Fuzzy LinPreRa. This study demonstrates that, in addition to reducing the number of pairwise comparisons, Fuzzy LinPreRa also increases decision making efficiency and accuracy.     

Hesitant fuzzy Bonferroni means for multi-criteria decision making

Due to the desirable characteristic of the Bonferroni mean (BM) that it can capture the interrelationship between input arguments, and in order to provide the properties and the modelling capability of the BMs under hesitant fuzzy environment, we explore some new hesitant fuzzy Bonferroni means (HFBMs). The properties and the special cases of HFBMs are studied in detail. We specially define a concept of hesitant Bonferroni element (HBE), which is considered as a ‘bonding satisfaction’ factor used as a calculation unit in the HFBM. The HBE can reflect the correlation between hesitant fuzzy arguments, which makes the HFBM have particular advantages in aggregating arguments. In addition, the weighted hesitant fuzzy Bonferroni mean (WHFBM) is also proposed considering different importance degrees of input arguments. Furthermore, the procedure of multi-criteria decision making based on the WHFBM is given under hesitant fuzzy environment as typical applications, which has much sense in theory and practice for the BM.