Selecting the advanced manufacturing technology using fuzzy multiple attributes group decision making with multiple fuzzy information

Selection of advanced manufacturing technology in manufacturing system management is very important to determining manufacturing system competitiveness. This research develops a fuzzy multiple attribute decision-making applied in the group decision-making to improving advanced manufacturing technology selection process. Since numerous attributes have been considered in evaluating the manufacturing technology suitability, most information available in this stage is subjective, imprecise and vague, fuzzy sets theory provides a mathematical framework for modeling imprecision and vagueness. In the proposed approach, a new fusion method of fuzzy information is developed to managing information assessed in different linguistic scales (multi-granularity linguistic term sets) and numerical scales. The flexible manufacturing system adopted in the Taiwanese bicycle industry is employed in this study to demonstrate the computational process of the proposed method. Finally, sensitivity analysis can be performed to examine that the solution robustness.     

A multiple attributes decision making method using individual and collaborative attribute data in a fuzzy environment

Although multiple attribute decision making (MADM) problems with both individual attribute data of a single alternative and collaborative attribute data of pairwise alternatives exist in the real world, they have seldom been a focus of research. This paper proposes a MADM method using individual and collaborative attribute data in a fuzzy environment, in which experts use linguistic variables to express their opinions. In the method, first, the evaluation matrix of individual attributes date and the judgment matrix of collaborative attributes data are constructed. Then, the central dominance of one alternative outranking other all alternatives is defined for aggregating the collaborative data. From this, an integrated decision matrix incorporating individual and collaborative attribute data is constructed. Further, based on an extended TOPSIS, the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS) are determined, and the relative closeness of each alternative to the FPIS and FNIS is calculated to determine the ranking order of all alternatives. Finally, two examples are used to illustrate the applicability of the proposed method.     

Multiple criteria decision making and decision support systems — Guest editor's introduction

Integration of MCDM with DSS brings benefit to both fields. MCDM tools are useful in identifying and evaluating incompatible alternatives for DSS, while DSS can implement MCDM approaches and help maintain and retrieve MCDM models. Over the years, MCDM has made considerable contribution to the development of various DSS subspecialties. This special issue on Multiple Criteria Decision Making and Decision Support Systems consists of 9 selected papers from the 20th International Conference on Multiple Criteria Decision Making. The guest editors highlight the key ideas and contributions of the papers in the special issue.     

Fuzzy linear programming approach to multiattribute decision making with multiple types of attribute values and incomplete weight information

In the classical Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), the decision maker (DM) gives the pair-wise comparisons of alternatives with crisp truth degree 0 or 1. However, in the real world, DM is not sure enough in all comparisons and can express his/her opinion with some fuzzy truth degree. Thus, DM's preferences are given through pair-wise comparisons of alternatives with fuzzy truth degrees, which may be represented as trapezoidal fuzzy numbers (TrFNs). Considered such fuzzy truth degrees, the aim of this paper is to develop a new fuzzy linear programming technique for solving multiattribute decision making (MADM) problems with multiple types of attribute values and incomplete weight information. In this method, TrFNs, real numbers, and intervals are used to represent the multiple types of decision information. The fuzzy consistency and inconsistency indices are defined as TrFNs due to the alternatives’ comparisons with fuzzy truth degrees. Hereby a new fuzzy linear programming model is constructed and solved by the possibility linear programming method with TrFNs developed in this paper. The fuzzy ideal solution (IS) and the attribute weights are then obtained. The distances of alternatives from the fuzzy IS can be calculated to determine their ranking order. The implementation process of the method proposed in this paper is illustrated with a strategy partner selection example. The comparison analyzes show that the method proposed in this paper generalizes the classical LINMAP, fuzzy LINMAP and possibility LINMAP.     

Linguistic continuous ordered weighted distance measure and its application to multiple attributes group decision making

This paper develops a new method for group decision making and introduces a linguistic continuous ordered weighted distance (LCOWD) measure. It is a new distance measure that combines the linguistic continuous ordered weighted averaging (LCOWA) operator with the ordered weighted distance (OWD) measure considering the risk attitude of decision maker. Moreover, it also can relieve the influence of extremely large or extremely small deviations on the aggregation results by assigning them smaller weights. These advantages make it suitable to deal with the situations where the input arguments are represented with uncertain linguistic information. Some of the main properties of the LCOWD measure and different particular cases are studied. The applicability of the new approach is also analyzed focusing on a group decision making problem.     

通信网络的运行可靠性评估新方法

通信网络的可靠性在通信网络管理及决策中发挥着重要作用。传统的通信网络可靠性分析和评估方法大多只考虑了某一方面性能或指标,无法全面准确地进行评估。根据可靠性理论和系统工程思想,提出了基于多指标决策的通信网络运行可靠性评估模型。该模型针对评估指标的相对性、模糊性和灰色性,运用灰色关联投影法定量计算得到通信网络运行可靠性的综合评估值,并依此对通信网络进行排序和优选;采用熵值法客观地确定指标权重,避免了人为主观因素的影响。通过两个实例验证了该模型的工程实用性,其评估结果表明灰色关联投影法比模糊灰关联法和熵权双基法更有效。     

A problem structuring front end for a multiple criteria decision support system

Structuring a problem is a key part of decision making. For multiple criteria decision problems, defining the criteria is an important element of the structuring process. To provide a decision maker with a general instrument for identifying relevant criteria, two methods representing different approaches were empirically evaluated. This evaluation showed that Kelly's repertory grid technique in particular has several useful features. Accordingly, the repertory grid technique was adapted to build a problem structuring front end for the aspiration-level interactive method proposed by Lotfi, Stewart, and Zionts.     

An automatic approach to reaching consensus in multiple attribute group decision making

In this work, we consider the problem of consensus of multiple attribute group decision making, and develop an automatic approach to reaching consensus among group opinions. In the process of group decision making, each expert provides his/her preferences over the alternatives with respect to each attribute, and constructs an individual decision matrix. The developed approach first aggregates these individual decision matrices into a group decision matrix by using the additive weighted aggregation (AWA) operator, and then establishes a convergent iterative algorithm to gain a consentaneous group decision matrix. Then based on the consentaneous group decision matrix, the approach utilizes the AWA operator to derive the overall attribute values of alternatives, by which the most desirable alternative can be found out. Finally, we detailedly expound the implementation process of the approach with a practical example.     

混合型多属性决策问题的序关系求解方法

研究了包含实数型、区间型和风险型属性的混合型多属性决策问题,提出一种基于优先序关系的决策模型.并给出了具体求解方法和步骤说明,通过比较各属性下方案间的优劣程度,建立模糊互补判断矩阵,以排序向量为基础计算每个方案各属性下的Borda分,根据属性权重向量加权集结得到总排序向量,实现方案总体优劣的排序,该方法直观、概念明确,不需要对决策矩阵进行规范化,易于实际操作.最后以自动目标识别系统性能评估为例,进行了算例分析.     

Dynamic fuzzy OWA model for group multiple criteria decision making

Obtaining relative weights in MCDM problems is a very important issue. The Ordered Weighted Averaging (OWA) aggregation operators have been extensively adopted to assign the relative weights of numerous criteria. However, previous aggregation operators (including OWA) are independent of aggregation situations. To solve the problem, this study proposes a new aggregation model – dynamic fuzzy OWA based on situation model, which can modify the associated dynamic weight based on the aggregation situation and can work like a “magnifying lens” to enlarge the most important attribute dependent on minimal information, or can obtain equal attribute weights based on maximal information. Two examples are adopted in this paper for comparison and showing the effects under different weights.     

Possibilistic programming approach for fuzzy multidimensional analysis of preference in group decision making

Multiple attribute decision making (MADM) problems are the most encountered problems in decision making. Fuzziness is inherent in decision making process and linguistic variables are well suited to assessing an alternative on qualitative attributes using fuzzy rating. A few techniques in MADM assess the weights of attributes based on preference information on alternatives. But they are not practical any more when the set of all paired comparison judgments from decision makers (DMs) on attributes are not crisp and also we have to deal with fuzzy decision matrix. This paper investigates the generation of a possibilistic model for multidimensional analysis of preference (LINMAP). The model assesses the fuzzy weights as well as locating the ideal solution with fuzzy decision making preference on attributes and fuzzy decision matrix. All of the information is assumed as triangular fuzzy numbers (TFNs). This method is developed in group decision making environments and formulates the problem as a possibilistic programming with multiple objectives.     

An enhanced method and its application for fuzzy multi-criteria decision making based on vague sets

Ye [Ye Jun. Improved method of multicriteria fuzzy decision making based on vague sets. Computer-Aid Design 2007;39:164–9] presented an improved method to handle multi-criteria fuzzy decision-making problems based on vague set theory. He/She provided some functions to measure the degree of suitability of each alternative with respect to a set of criteria presented by vague values. However, in some cases, these functions do not give sufficient information about alternatives. Therefore, in this paper, an enhanced method is provided to measure the accuracy membership of each alternative so as to give additional information for the decision maker. In addition, to making computing and ranking results easier and to increase the recruiting productivity, a computer-based decision-support system is also developed, which may help to make a decision more efficiently.     

An approach for multiple attribute group decision making problems with interval-valued intuitionistic trapezoidal fuzzy numbers

This article proposes an approach to resolve multiple attribute group decision making (MAGDM) problems with interval-valued intuitionistic trapezoidal fuzzy numbers (IVITFNs). We first introduce the cut set of IVITFNs and investigate the attitudinal score and accuracy expected functions for IVITFNs. Their novelty is that they allow the comparison of IVITFNs by taking into accounting of the experts’ risk attitude. Based on these expected functions, a ranking method for IVITFNs is proposed and a ranking sensitivity analysis method with respect to the risk attitude is developed. To aggregate the information with IVITFNs, we study the desirable properties of the interval-valued intuitionistic trapezoidal fuzzy weighted geometric (IVITFWG) operator, the interval-valued intuitionistic trapezoidal fuzzy ordered weighted geometric (IVITFOWG) operator, and the interval-valued intuitionistic trapezoidal fuzzy hybrid geometric (IVITFHG) operator. It is worth noting that the aggregated value by using these operators is also an interval-valued intuitionistic trapezoidal fuzzy value. Then, based on these expected functions and aggregating operators, an approach is proposed to solve MAGDM problems in which the attribute values take the form of interval-valued intuitionistic fuzzy numbers and the expert weights take the form of real numbers. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.     

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 expert’s 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 consistency and consensus based decision support model for group decision making with multiplicative preference relations

In group decision making (GDM) with multiplicative preference relations (also known as pairwise comparison matrices in the Analytical Hierarchy Process), to come to a meaningful and reliable solution, it is preferable to consider individual consistency and group consensus in the decision process. This paper provides a decision support model to aid the group consensus process while keeping an acceptable individual consistency for each decision maker. The concept of an individual consistency index and a group consensus index is introduced based on the Hadamard product of two matrices. Two algorithms are presented in the designed support model. The first algorithm is utilized to convert an unacceptable preference relation to an acceptable one. The second algorithm is designed to assist the group in achieving a predefined consensus level. The main characteristics of our model are that: (1) it is independent of the prioritization method used in the consensus process; (2) it ensures that each individual multiplicative preference relation is of acceptable consistency when the predefined consensus level is achieved. Finally, some numerical examples are given to verify the effectiveness of our model.     

Induced continuous Choquet integral operators and their application to group decision making

With respect to multi-attribute group decision making, in this study two induced continuous Choquet integral operators named as the induced continuous Choquet weighted averaging (ICCWA) operator and the induced continuous Choquet geometric mean (ICCGM) operator are defined, which reflect the interactive characteristics between elements. Meantime, some associated desirable properties are studied to provide assurance in applications. In order to globally reflect the interactions between elements, we further define the probabilistic generalized semivalue ICCWA (PGS-ICCWA) operator and the probabilistic generalized semivalue ICCGM (PGS-ICCGM) operator. If the information about the weights of experts and attributes is incompletely known, the models for the optimal fuzzy measures on experts set and on attribute set based on consistency principle and TOPSIS method are respectively established. Moreover, an approach to uncertain multi-attribute group decision making with incomplete weight information and interactive conditions is developed. Finally, a numerical example is provided to illustrate the practicality and feasibility of the developed procedure.     

Fuzzy methods for case-based recommendation and decision support

The paper proposes two case-based methods for recommending decisions to users on the basis of information stored in a database. In both approaches, fuzzy sets and related (approximate) reasoning techniques are used for modeling user preferences and decision principles in a flexible manner. The first approach, case-based decision making, can principally be seen as a case-based counterpart to classical decision principles well-known from statistical decision theory. The second approach, called case-based elicitation, combines aspects from flexible querying of databases and case-based prediction. Roughly, imagine a user who aims at choosing an optimal alternative among a given set of options. The preferences with respect to these alternatives are formalized in terms of flexible constraints, the expression of which refers to cases stored in a database. As both types of decision support might provide useful tools for recommender systems, we also place the methods in a broader context and discuss the role of fuzzy set theory in some related fields.     

A view of group decision making process and bivoting approach

Many characteristics of Group Decision Making (GDM) are different from those of individual decision. The literature dealing with the stages of GDM is rather scant. This paper presents a view about the stages of group decision processes, characterizing GDM as a dynamic process. We discuss particularly the aggregation stage of preferences and reports a bivoting approach' used in practising group decision support systems (GDSS). Finally, a concrete example is given to illustrate each stage of group decision process and tile 'bivoting approach' in consensus reaching stage.