Group consensus measurement in MADM with multiple preference formats

An approach is proposed for measuring the group consensus in multiple attribute decision-making (MADM) problems with experts’ various preference information on alternatives. In the approach, multiple decision-makers give their preference information on alternatives in different formats. The uniformities and aggregation process with fuzzy majority method are employed to obtain the social fuzzy preference relation on the alternatives. Accordingly, the ranking values of the alternatives are obtained based on the obtained individual expert’s fuzzy preference relation, and the social one. The group consensus can be measured based on the ranking values of the alternatives that are derived from the individual expert’s preference information and the social one. An example of selecting robots is presented as an illustration

Granular computing and optimization model-based method for large-scale group decision-making and its application

In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare some alternatives, so they often express evaluation information with incomplete hesitant fuzzy linguistic preference relations. How to obtain suitable large-scale group decision-making results from incomplete preference information is an important and interesting issue to concern about. After analyzing the existing researches, we find that: i) the premise that complete preference relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given by decision makers are greatly possible to be changed during the consistency improving process. In order to solve these issues, this work proposes a novel method based on Granular computing and optimization model for large-scale group decision-making, considering the original consistency of incomplete hesitant fuzzy linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the rationality and advantages of the proposed method: i) semantics are not changed during the consistency improving process, ii) completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally consistent, iv) final large-scale group decision-making result is acquired by fusing complete preference relations with different weights

Hesitant Probabilistic Fuzzy Preference Relations in Decision Making

Preference of an alternative over another alternative is a useful way to express the opinion of decision maker. In the process of group decision making, preference relations are used in preference modelling of the alternatives under given criteria. The probability is an important tool to deal with uncertainty; in many scenarios of decision making probabilities of different events affect the decision making process directly. In order to deal with this issue, in this paper, hesitant probabilistic fuzzy preference relation (HPFPR) is defined. Furthermore, consistency of HPFPR and consensus among decision makers are studied in the hesitant probabilistic fuzzy environment. In this respect, many novel algorithms are developed to achieve consistency of HPFPRs and reasonable consensus between decision makers and a final algorithm is proposed comprehending all other algorithms, presenting a complete decision support model for group decision making. Lastly, we present a case study with complete illustration of the proposed model and discussed the effects of probabilities on decision making validating the importance of the introduction of probability in hesitant fuzzy preference relation

Intuitionistic Trapezoidal Fuzzy Multiple Criteria Group Decision Making Method Based on Binary Relation

The aim of this paper is to develop a methodology for intuitionistic trapezoidal fuzzy multiple criteria group decision making problems based on binary relation. Firstly, the similarity measure between two vectors based on binary relation is defined, which can be utilized to aggregate preference information. Some desirable properties of the similarity measure based on fuzzy binary relation are also studied. Then, a methodology for fuzzy multiple criteria group decision making is proposed, in which the criteria values are in the terms of intuitionistic trapezoidal fuzzy numbers (ITFNs). Simple and exact formulas are also proposed to determine the vector of the aggregation and group set. According to the weighted expected values of group set, it is easy to rank the alternatives and select the best one. Finally, we apply the proposed method and the Cosine similarity measure method to a numerical example; the numerical results show that our method is effective and practical

Incomplete interval fuzzy preference relations for supplier selection in supply chain management

In the analytical hierarchy process (AHP), it needs the decision maker to establish a pairwise comparison matrix requires n(n–1)/2 judgments for a level with n criteria (or alternatives). In some instances, the decision maker may have to deal with the problems in which only partial information and uncertain preference relation is available. Consequently, the decision maker may provide interval fuzzy preference relation with incomplete information. In this paper, we focus our attention on the investigation of incomplete interval fuzzy preference relation. We first extend a characterization to the interval fuzzy preference relation which is based on the additive transitivity property. Using the characterization, we propose a method to construct interval additive consistent fuzzy preference relations from a set of n–1 preference data. The study reveals that the proposed method can not only alleviate the comparisons, but also ensure interval preference relations with the additive consistent property. We also develop a novel procedure to deal with the analytic hierarchy problem for group decision making with incomplete interval fuzzy preference relations. Finally, a numerical example is illustrated and a supplier selection case in supply chain management is investigated using the proposed method. First published online: 05 Feb 201

Penggunaan metode non numerik serta semi numerik dalam masalah pengambilan keputusan kelompok yang berbasis pada relasi preferensi fuzzy = The Use of Non-Numeric and Semi-Numeric Methods in Group Decision Making Problems

In real world there are many cases where decisions taken must consider many opinions from more than one decision maker, which then shape the concept of group decision making. In group decision making, there are many methods introduced by experts, especially which based on fuzzy preference relation. On this research, it will be discussed the non, numeric method and semi numeric method for group decision making problems, with linguistic label for its preference. The problems appear are, when and on what condition one of both method is more suitable to be used? For that reason, it is necessary to find out the characteristics of each methods through testing done in this research. This research is aimed to analyze consistency of the solution resulted from non-numeric method as well as semi-numeric method when some different input characteristics are entered, and to analyze the sensitivity level of both methods using sensitivity analysis. It is expected that by this sensitivity analysis, it can answer when some result inconsistency happen on both methods. It can find out when and on what condition one of both methods is more suitable to be used Keywords : Non-numeric method, semi-numeric method, group Decision Making, fuzzy preference relation, sensitivity analysi

New fuzzy preference relations and its application in group decision making

Decision making preferences to certain criteriabusually focus on positive degrees without considering the negative degrees. However, in real life situation, evaluation becomes morebcomprehensive if negative degrees are considered concurrently. Preference is expected to be more effective when considering both positive and negative degrees of preference to evaluate the best selection. Therefore, the aim of this paper is to propose the conflicting bifuzzy preference relations in group decision making by utilization of a novel score function. The onflicting bifuzzy preference relation is obtained by introducing some modifications on intuitionistic fuzzy preference relations. Releasing the intuitionistic condition by taking into account positive and negative degrees simultaneously and utilizing the novel score function are the main modifications to establish the proposed preference model. The proposed model is tested with a numerical example and proved to be simple and practical. The four-step decision model shows the efficiency of obtaining preference in group decision making

A problem solving perspective on evaluating knowledge management technologies: Using fuzzy linear programming technique for multiattribute group decision making with fuzzy decision variables

Erensal, Yasemin Claire (Dogus Author) -- Conference full title: PICMET Conference: Technology Management for the Global Future : Istanbul, Turkey, 8 - 13 July 2006The aim of this paper is to develop a framework to aid in the evaluation and selection of KM tools and technologies. In this paper, we investigate the fuzzy linear programming technique (FLP) for multiple attribute group decision making (MAGDM) problems with preference information on alternatives. To reflect the decision maker's subjective preference information and to determine the weight vector of attributes, the linear programming technique for multidimensional analysis of preference (LINMAP) is used. The LINMAP method is based on pairwise comparisons of alternatives given by decision makers and generates the best compromise alternative as the solution that has the shortest distance to the positive ideal solution. Our aim is to develop a LINMAP in MAGDM problem, where decision makers (DM) give their preferences on alternatives in a fuzzy relation. Through the proposed methodology in this research, enterprises can reduce the mismatch between the capability and implementation of the KM technology, and greatly enhance the effectiveness of implementation of the KMS. Finally, the developed model is applied to a real case of assisting decisionmakers in a leading logistics company in Turkey to illustrate the use of the proposed method