Zhi-Ping Fan

A subjective and objective integrated approach to determine attribute weights

This paper proposes an integrated approach to determine attribute weights in the multiple attribute decision making (MADM) problems. The approach makes use of the subjective information provided by a decision maker and the objective information to form a two-objective programming model. Thus the resultant attribute weights and rankings of alternatives reflect both the subjective considerations of a decision maker (DM) and the objective information. An example is used to illustrate the applicability of the proposed approach.

A method for repairing the inconsistency of fuzzy preference relations

This paper investigates the inconsistency problems of preference information about alternatives expressed as a fuzzy preference relation by a decision maker. An analysis method is presented to identify the inconsistency and weak transitivity of a fuzzy preference relation and to repair its inconsistency to reach weak transitivity. First, several definitions are given on additive consistency, inconsistency and weak transitivity of a fuzzy preference relation. Next, two methods derived from graph theory are presented to judge whether a fuzzy preference relation has weak transitivity or not. Then, an algorithm is developed to repair an inconsistent fuzzy preference relation and to make it become one with weak transitivity, via a synthesis matrix which reflects the relationship between the fuzzy preference relation with additive consistency and the initial one given by a decision maker. A convergence theorem is also given for the algorithm. Lastly, two numerical examples are shown to illustrate the proposed method.

A method for group decision making with multi-granularity linguistic assessment information

This paper proposes a method to solve the group decision making (GDM) problems with multi-granularity linguistic assessment information. In the method, the multi-granularity linguistic information provided by experts is firstly expressed in the form of fuzzy numbers. In order to make the collective opinion close to each experts opinion, a linear goal programming model is constructed to integrate the fuzzy assessment information and to directly compute the collective ranking values of alternatives without the need of information transformation. Then, a fuzzy preference relation on the pairwise comparisons of the collective ranking values of alternatives is constructed using the dominance possibility degree of the comparison between the fuzzy numbers. By applying a non-dominance choice degree to this fuzzy preference relation, the ranking of alternatives is determined and the most desirable alternative(s) is selected. An example is used to illustrate the applicability of the proposed method and its advantages.

A goal programming approach to group decision making based on multiplicative preference relations and fuzzy preference relations

Abstract This paper proposes a goal programming approach to solve group decision-making (GDM) problems where the preference information on alternatives provided by decision makers is represented in two different formats, i.e. multiplicative preference relations and fuzzy preference relations. In order to narrow the gap between the collective opinion and each decision maker’s opinion, a linear goal programming model is constructed to integrate the two different formats of preference relations and to compute the collective ranking values of the alternatives. Thus, the ranking of alternatives or selection of the most desirable alternative(s) is obtained directly from the computed collective ranking values. A numerical example is also used to illustrate the applicability of the proposed approach.

Ranking L-R fuzzy number based on deviation degree

This paper proposed a novel approach to ranking fuzzy numbers based on the left and right deviation degree (L-R deviation degree). In the approach, the maximal and minimal reference sets are defined to measure L-R deviation degree of fuzzy number, and then the transfer coefficient is defined to measure the relative variation of L-R deviation degree of fuzzy number. Furthermore, the ranking index value is obtained based on the L-R deviation degree and relative variation of fuzzy numbers. Additionally, to compare the proposed approach with the existing approaches, five numerical examples are used. The comparative results illustrate that the approach proposed in this paper is simpler and better.

Evaluating knowledge management capability of organizations: a fuzzy linguistic method

Knowledge management capability (KMC) is the source for organizations to gain the sustainable competitive advantage. KMC evaluation is a required work with strategic significance. However it still has not been addressed in the existing literatures. So the objective of this study is to investigate a fuzzy multiple attributes decision-making method (FMADM) for evaluating KMC. In this paper, a framework for evaluating KMC is presented, which includes two parts, one is an evaluation hierarchy with attributes, the other a judgment matrix model with two dimensions to identify the evaluation results of KMC. Then, a fuzzy linguistic approach is proposed to evaluate the KMC of organizations. The evaluation results of KMC obtained through the proposed approach are objective and unbiased due to two reasons. Firstly, the results are generated by a group of experts in the presence of motile attributes. Secondly, the fuzzy linguistic approach employed in this paper has more advantage to reduce distortion and losing of information than other fuzzy linguistic approaches. Through evaluation result of KMC, managers could judge the necessity to improve the KMC and determine which dimension of KMC is the most needed direction to improve. Additionally, an example is used to illustrate the availability of the proposed method.

A method for group decision-making based on multi-granularity uncertain linguistic information

The multi-granularity uncertain linguistic term is a form of uncertain preference information in group decision-making (GDM), while it is seldom discussed in the existing research. In this paper, a method is proposed to solve the GDM problem with multi-granularity uncertain linguistic information. Firstly, to process multi-granularity uncertain linguistic information, a formula for transforming multi-granularity uncertain linguistic terms into trapezoidal fuzzy numbers is given based on the theoretical analysis. Thus, the GDM problem with multi-granularity uncertain linguistic information is changed into the one with fuzzy numbers. Then, to solve the GDM problem, an appropriate extension of the classical TOPSIS is conducted. Fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) are defined, respectively. The closeness coefficient is obtained to determine the ranking order of all alternatives by calculating the distances to both FPIS and FNIS, simultaneously. Finally, a numerical example is given to illustrate the use of the proposed method.

A Group Decision Support Approach to Evaluate Experts for R&D Project Selection

In R&D project selection, experts (or external reviewers) always play a very important role because their opinions will have great influence on the outcome of the project selection. It is also undoubted that experts with high-expertise level will make useful and professional judgments on the projects to be selected. So, how to measure the expertise level of experts and select the most appropriate experts for project selection is a very significant issue. This paper presents a group decision support approach to evaluate experts for R&D project selection. Where the criteria and their attributes for evaluating experts are summarized mainly based on the experience with the National Natural Science Foundation of China (NSFC). A formal procedure that integrates both objective and subjective information on experts is also presented. It is mainly based on analytic hierarchy process (AHP), scoring method, and fuzzy linguistic processing. A group decision support system is designed and implemented for illustration of the proposed method.

A chi-square method for obtaining a priority vector from multiplicative and fuzzy preference relations

Abstract Decision makers (DMs)’ preferences on decision alternatives are often characterized by multiplicative or fuzzy preference relations. This paper proposes a chi-square method (CSM) for obtaining a priority vector from multiplicative and fuzzy preference relations. The proposed CSM can be used to obtain a priority vector from either a multiplicative preference relation (i.e. a pairwise comparison matrix) or a fuzzy preference relation or a group of multiplicative preference relations or a group of fuzzy preference relations or their mixtures. Theorems and algorithm about the CSM are developed. Three numerical examples are examined to illustrate the applications of the CSM and its advantages.

Extended TODIM method for hybrid multiple attribute decision making problems

TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) is a method for solving the multiple attribute decision making (MADM) problem considering decision makers (DMs) behavior, in which the attribute values are in the format of crisp numbers. It cannot be used to handle hybrid MADM problems with various formats of attribute values. In this paper, an extended TODIM method is proposed to solve the hybrid MADM problem. First, three formats of attribute values (crisp numbers, interval numbers and fuzzy numbers) are expressed in the format of random variables with cumulative distribution functions. Then, according to the concept of the classical TODIM method, the gain and loss matrices concerning each attribute are constructed by calculating the gain and loss of each alternative relative to the others. Further, by calculating the dominance degree of each alternative over the others, the overall value of each alternative can be obtained to rank the alternatives. Finally, two numerical examples are used to illustrate the use of the proposed method.