Multi-attribute Group Decision-making Approach Research Articles

A variable weight‐based hybrid approach for multi‐attribute group decision making under interval‐valued intuitionistic fuzzy sets

This article aims to develop a novel hybrid multi-attribute group decision-making approach under interval-valued intuitionistic fuzzy sets (IVIFS) by integrating variable weight, correlation coefficient, and technique for order performance by similarity to an ideal solution (TOPSIS). First, experts give their evaluation in IVIFS, and then the weighting evaluation matrix is computed based on interval-valued intuitionistic fuzzy weighted averaging operator with the subjective attribute weights given in advance. Second, a simple and useful weighting approach on the basis of correlation coefficient is put forward to obtain the experts weights. Third, we treat the attribute weights as a varying vector, and then propose a variable weighting approach for its acquisition. Fourth, an individual decision can be converted to an alternative decision by considering the experts and attributes weights together. At last, the integrated assessment value of each alternative is computed by TOPSIS, and then the most appropriate alternative is chosen. Two illustrative examples dealt with the problem by the method presented in this article demonstrate the usefulness of this approach, compared with those by the other methods.

Bid evaluation in civil construction under uncertainty: A two-stage LSP-ELECTRE III-based approach

Increasing growth and development of building industry and civil engineering motivates enterprises to advance bid evaluation process in an effort to ameliorate building quality and diminish construction cost. The selection of qualified construction contractor has been generally formulated and addressed as a multi-attribute group decision-making problem. However, few existing studies have taken the reasoning logic pattern in human cognition process and the compensatory among attributes into account. Therefore, a two-stage logic scoring of preference-ELECTRE III-based approach is established for the management and manipulation of bidder selection. In a bid to facilitate expert evaluation articulation, alternative-attribute assessments in this approach take the form of generalized comparative linguistic expressions, which are subsequently converted into possibility-distribution-based hesitant fuzzy linguistic term set via the similarity measure-based generation approach to enhance information quality and reliability. Then, nonlinear optimization-based direction rules are introduced to advance the automatic minimum-distance-based consensus reaching model and based on which, the individual semantic-embedded assessments are algorithmically adjusted to reach acceptable consensus. Furthermore, the expert and attribute importance degrees are extracted from both semantic and formal logic aspects, and the modified individual assessments are gathered by integrating the two-stage logic scoring of preference aggregation structure and the ELECTRE III method. The present approach provides a solution to bid evaluation problems considering the reasoning logic pattern in human cognition process and the compensatory among attributes. A practical case study and a comparative analysis are performed to demonstrate the feasibility and effectiveness of the established multi-attribute group decision-making approach.

A Multi-Attribute Group Decision-Making Method Based on Linguistic Intuitionistic Fuzzy Numbers and Dempster–Shafer Evidence Theory

In this paper, we propose a multi-attribute group decision-making (MAGDM) method based on Dempster–Shafer Evidence Theory (DST) and linguistic intuitionistic fuzzy numbers (LIFNs), in which both the expert weights and attribute weights are unknown. Firstly, we represent LIFNs as basic probability assignments (BPAs) by DST based on linguistic scale function (LSF), and a linear programming model is proposed to combine the objective weights and subjective weights of attributes to obtain the combined weights. At the same time, the experts’ weights are obtained through Jousselme distance. Secondly, we use the weights to correct the evidence, and the comprehensive evaluation value of each alternative is calculated by the combination rule of evidence. Further, a new MAGDM approach with DST and LIFNs is presented. Finally, we give an example to explain the proposed method and compare it with other methods to show the feasibility and superiority.

Novel neutrality aggregation operator-based multiattribute group decision-making method for single-valued neutrosophic numbers

The paper aims to give some new kinds of operational laws named as neutrality addition and scalar multiplication for the pairs of single-valued neutrosophic numbers. The main idea behind these operations is to include the neutral characters of the decision-maker towards the preferences of the objects when it shows the equal degrees to membership functions. Some salient features of them are investigated also. Further based on these laws, some new aggregation operators are developed to aggregate the different preferences of the decision-makers. Desirable relations and properties are investigated in detail. Finally, a multiattribute group decision-making approach based on the proposed operators is presented and investigated with numerous numerical examples. The superiors, as well as the advantages of the operators, are also discussed in it.

A multi‐agent affective interactive MAGDM approach and its applications

AbstractTraditional multi‐attribute group decision‐making (MAGDM) methods focus on weights calculation of sub‐attributes and experts' preferences, but lack the discussion on the decision‐makers' affective interaction, and its influence on the decision preference and group consistency. To address this problem, the present study proposed a new multilayer affective computing model based on “personality–mood–emotion” pattern, under the multi‐agent decision system framework. In addition, we introduced the group trending index and affection‐preference incentive mechanism, which can help simulate MAGDM process and learn group experts' decision preferences. Further, we proposed a new multi‐agent affective interactive MAGDM (MAAI‐MAGDM) method, where we defined a novel group convergence index and an alternative decision entropy to explain the convergence process of decision and group consistency. Compared to the traditional MAGDM approaches, the proposed MAAI‐MAGDM method fully considered the affective features of each expert, reduced the dependence on aggregation operators and weight analysis, alleviated the workload of group experts, and effectively reduced the complexity of decision‐making calculation process. Finally, we verified that the proposed method can effectively assist the decision‐making processes by employing two numerical cases.

A Competitive Multiattribute Group Decision-Making Approach for the Game between Manufacturers.

Under the competitive market environment, the game between manufacturers comes down to the competitive multiattribute group decision-making problem. In this study, the evaluation information of experts is given in the form of 2-dimension 2-tuple linguistic variables, and an approach is proposed for the competitive multiattribute group decision-making problem based on game theory and evidence theory. Firstly, based on the evidence theory, the attribute values of each situation are obtained by aggregating the 2-dimension 2-tuple linguistic evaluation information given by experts. Secondly, according to the attribute values of every situation, the evidence theory is applied for the second aggregation to obtain the overall values of every situation, and then the game matrix of competitive multiattribute group decision problem is formed. Then, according to the bivariate game matrix, the Nash equilibrium point of competitive multiattribute group decision-making problem is determined based on game theory. Finally, a practical case about the alternative selections for a duopoly problem is used to illustrate the effectiveness and applicability of the proposed approach for the competitive multiattribute group decision-making problem.

A new multi-criteria group decision-making approach based on q-rung orthopair fuzzy interaction Hamy mean operators

The recently proposed q-rung orthopair fuzzy set (q-ROFS) is a powerful and effective tool to describe uncertainty and vagueness, and Hamy mean (HM) has a significant advantage of capturing the interrelationship among aggregated arguments. In order to take full advantage of q-ROFS and HM, and consider the interactions between membership and non-membership degrees at the same time, in this paper, we propose a family of q-rung orthopair fuzzy Hamy mean operators based on interaction operations. First, we define interaction operational rules for q-rung orthopair fuzzy numbers. Based on the new operational rules, q-rung orthopair fuzzy interaction HM and q-rung orthopair fuzzy interaction weighted HM operators are proposed. Further, we propose a dual Hamy mean (DHM) operator and extend it to accommodate q-rung orthopair fuzzy environment. Based on interaction operational rules and DHM, q-rung orthopair fuzzy interaction DHM operator and its weighted form are also developed. Then, a novel multi-attribute group decision-making approach based on proposed operators is introduced. Finally, a numerical instance, as well as some comparative analyses, is provided to illustrate the validity and advantages of the new approach.

Failure Mode and Effect Analysis in a Linguistic Context: A Consensus-Based Multiattribute Group Decision-Making Approach

Failure mode and effect analysis (FMEA) is an effective risk-management tool, which has been extensively utilized to manage failure modes (FMs) of products, processes, systems, and services. Almost all FMEA models are concerned with how to get a complete risk order of FMs from highest to lowest risk. However, in many situations, it may be sufficient to classify the FMs into several ordinal risk classes. Meanwhile, generating a consensual decision is crucial for the FMEA problem because 1) reaching consensus will enhance the connections among FMEA participants, and 2) a highly accepted group solution to the FMEA problem can be generated. Thus, this study proposes a consensus-based group decision-making framework for FMEA with the aim of classifying FMs into several ordinal risk classes in which we assumed that FMEA participants provide their preferences in a linguistic way using possibilistic hesitant fuzzy linguistic information. In the FMEA framework, a consensus-driven methodology is presented to generate the weights of risk factors. Following this, an optimization-based consensus rule guided by a minimum adjustment distance policy is devised, and an interactive model for reaching consensus is developed to generate consensual FM risk classes. In order to justify its validity of the proposal, our framework is applied for the risk evaluation of proton beam radiotherapy.

A Novel Multi-Attribute Group Decision-Making Approach in the Framework of Proportional Dual Hesitant Fuzzy Sets

Making decisions are very common in the modern socio-economic environments. However, with the increasing complexity of the social, today’s decision makers (DMs) face such problems in which they hesitate and irresolute to provide their views. To cope with these uncertainties, many generalizations of fuzzy sets are designed, among them dual hesitant fuzzy set (DHFS) is quite resourceful and efficient in solving problems of a more vague nature. In this article, a novel concept called proportional dual hesitant fuzzy set (PDHFS) is proposed to further improve DHFS. The PDHFS is a flexible tool composed of some possible membership values and some possible non-membership values along with their associated proportions. In the theme of PDHFS, the proportions of membership values and non-membership values are considered to be independent. Some basic operations, properties, distance measure and comparison method are studied for the proposed set. Thereafter, a novel approach based on PDHFSs is developed to solve problems for multi-attribute group decision-making (MAGDM) in a fuzzy situation. It is totally different from the traditional approach. Finally, a practical example is given in order to elaborate the proposed method for the selection of the best alternative and detailed comparative analysis is given in order to validate the practicality.

Multiattribute Group Decision-Making Approach With Linguistic Pythagorean Fuzzy Information

The purpose of this study is to construct the multi-attribute group decision making (MAGDM) approach with linguistic Pythagorean fuzzy information (LPFI) based on generalized linguistic Pythagorean fuzzy aggregation operators (GLPFA). To begin with, we define the generalized indeterminacy degree-preference distance of linguistic Pythagorean fuzzy numbers (LPFNs), on the basis of it, we build a new approach for ranking the alternatives after analysing the existed comparison rule. In addition, we introduce the new version of t-norms (TNs) and t-conorms (TCs) named linguistic Pythagorean t-norms (LPTNs) and linguistic Pythagorean t-conorms (LPTCs), which can be used to handle the LPFI; some special cases for LPTNs and LPTCs are obtained and they can deal with Pythagorean fuzzy information (PFI). Thirdly, we introduce the generalized linguistic Pythagorean fuzzy average aggregation operator (GLPFAA) based on LPTN and LPTC along with their properties are also investigated, whilst, some special cases of GLPAA are obtained when LPTN and LPTC take some special TNs and TCs. Finally, a MAGDM approach based on some LPTNs and LPTCs is constructed to deal with some MAGDM problems with unknown attributes'weights and experts' weights, before building the MAGDM approach, we define new cross-entropy to fix the experts's weights and use the maximizing deviation to calculate the attributes' weights based on the proposed indeterminacy degree-preference distance. Consequently, an illustrative example is provided in order to show the effectiveness and advantages of the proposed method and some comparisons are also carried out.

A Novel Multi-Attribute Group Decision-Making Method and Its Application in Solving the Downward Referral Problem in the Hierarchical Medical Treatment System in China

China has made many achievements in health care industry since the establishment of hierarchical medical treatment system, but also exposed a lot of problems, such as the problem of “easy to upward-referral, hard to downward-referral”, which occurred in the implementation process of two-way referral. Recently, the appearance of “hard to downward-referral” have gotten more and more attention from scholars. The key step in solving the problem of “downward-referral” is to classify hospitals at different levels evaluated by the professional doctors. This paper proposes a framework to solve the problem where evaluation values of the hospital are given as q-rung orthopair fuzzy uncertain linguistic variables (q-ROFULVs). Considering Schweizer-Sklar t-conorm and t-norm (SSTT) can make the information aggregation process more flexible, and Hamy Mean operator can consider the correlation between hospital indicators. In order to take full advantage of these two kinds of operators, firstly, we extend SSTT to q-ROFULVs and define Schweizer-Sklar operational rules of q-ROFULVs. Secondly, we combine the Hamy Mean operator with Schweizer-Sklar t-conorm and t-norm, and propose the q-rung orthopair fuzzy uncertain linguistic Schweizer-Sklar Hamy mean aggregation operators and q-Rung orthopair fuzzy uncertain linguistic Schweizer-Sklar weighted Hamy Mean respectively. Afterwards, q-rung orthopair fuzzy uncertain linguistic Schweizer-Sklar dual Hamy mean operator and its weighted form are also developed. Then, we have studied in detail some of the ideal properties of these operators. Furthermore, a novel multi-attribute group decision-making approach based on proposed operators is introduced. Finally, we apply the new approach to solve the problem about the downward conversion in the hierarchical medical treatment system. This is of great significance for integrating the medical resources of the whole society and improving the service efficiency of the medical service system.

Group Decision Making With Unbalanced-Expertise

Problem complexities have forced the decision makings techniques to shift to a more dynamic approach. In this study, a mixed decision-making methodology is introduced which integrates multi-attribute decision-making (MADM) techniques with qualitative group decision making approaches (Delphi, Brainstorming, and Nominal group technique). In addition, this study proposes a generalizable new multi-attribute group decision-making approach, with a group of experts who have a non-equal level of expertise. Feasibility of the proposed approach is tested with a case of a factor rating (Weighted Score method) MADM.

Pythagorean fuzzy prioritized aggregation operators and their application to multi-attribute group decision making

Pythagorean fuzzy set is a useful tool to deal with the fuzziness and vagueness. Many aggregation operators have been proposed by many researchers based on Pythagorean fuzzy sets. But the current methods are under the assumption that the decision makers and the attributes are at the same priority level. However, in real group decision-making problems the attribute and decision makers may have different priority level. Therefore, in this paper, we develop multi-attribute group decision-making based on Pythagorean fuzzy sets where there exists a prioritization relationship over the attributes and decision makers. First, we develop Pythagorean fuzzy prioritized weighted average operator and Pythagorean fuzzy prioritized weighted geometric operator. Then we study some of its desirable properties such as idempotency, boundary and monotonicity in detail. Moreover, we propose a multi-attribute group decision-making approach based on the developed operators under Pythagorean fuzzy environment. Finally, a numerical example is provided to illustrate the practicality of the proposed approach.

Evaluation of Flexible Manufacturing Systems Using a Hesitant Group Decision Making Approach

Abstract Flexible manufacturing systems (FMS) are capable of processing various parts, styles, and quantities of production in manufacturing systems. It is a quite complex process for companies to decide the appropriate FMS design as it involves multiple and conflicting criteria and multiple decision makers under various uncertainties. The fuzzy set theory offers an efficient tool to cope with vagueness and to define performance measurement of FMS in a multi-attribute group decision making (MAGDM) framework. In this study, we present a MAGDM approach based on hesitant fuzzy sets to evaluate FMS in a fuzzy environment. A practical example is provided to demonstrate the proposed methodology. In addition, the performance of the method is assessed by a comparative study and sensitivity analysis. The results of the analysis show that the MAGDM approach is a useful tool for experts in terms of evaluation of FMS.

Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes

Cloud computing technology has become increasingly popular and can deliver a host of benefits. However, there are various kinds of cloud providers in the market and firms need scientific decision tools to judge which cloud computing vendor should be chosen. Studies in how a firm should select an appropriate cloud vendor have just started. However, existing studies are mainly from the technology and cost perspective, and neglect other influence factors, such as competitive pressure and managerial skills, etc. Hence, this paper proposes a multi-attribute group decision-making (MAGDM) based scientific decision tool to help firms to judge which cloud computing vendor is more suitable for their need by considering more comprehensive influencefactors. It is argued that objective attributes, i.e., cost, as well as subjective attributes, such as TOE factors (Technology, Organization, and Environment) should be considered for the decision making in cloud computing services, and presents a new subjective/objective integrated MAGDM approach for solving decision problems. The proposed approach integrates statistical variance (SV), improved techniques for order preference by similarity to an ideal solution (TOPSIS), simple additive weighting (SAW), and Delphi–AHP to determine the integrated weights of the attributes and decision-makers (DMs). The method considers both the objective weights of the attributes and DMs, as well as the subjective preferences of the DMs and their identity differences, thereby making the decision results more accurate and theoretically reasonable. A numerical example is given to illustrate the practicability and usefulness of the approach and its suitability as a decision-making tool for a firm using of cloud computing services. This paper enriches the theory and methodology of the selection problem of cloud computing vendoring and MAGDM analysis.

A Group Decision Making Approach for the Evaluation of Flexible Manufacturing Systems

In today’s competitive global market, manufacturing companies extremely compete to produce products of the highest quality, the lowest cost, and the best customer satisfaction. The survival of a company depends on the capable of tackling with variations and adapting changing conditions. In this respect, flexible manufacturing systems (FMS) are capable of processing various parts, styles and quantities of production in manufacturing systems. However; it is quite complex process for firms to decide the appropriate FMS design as it involves multiple and conflicting criteria and multiple decision makers under various uncertainties. The fuzzy set theory offers an efficient tool to cope with vagueness and to define performance measurement of FMS in a multi-attribute group decision making (MAGDM) framework. In this study, we present a MAGDM approach based on hesitant fuzzy sets to evaluate FMS in a fuzzy environment. A practical example is also provided to demonstrate the proposed methodology.

A Fuzzy Stochastic Multi-Attribute Group Decision-Making Approach for Selection Problems

The purpose of this paper is to demonstrate how the fuzzy and stochastic approaches as well as multi-attribute group decision-making techniques can be effectively utilized for selection problems in different kinds of uncertainty situations. A group of the decision makers describes a value for an alternative with respect to an attribute by the use of linguistic variables. The rating of each alternative is aggregated, which can be expressed as triangular fuzzy numbers. Then Monte Carlo simulation is applied to generate the performance distribution of alternatives with respect to different attributes in an uncertain environment. Finally, a stochastic compromise ranking technique, known as the VIKOR technique, is developed to evaluate probability distributions for each alternative on each attribute and to prioritize them. The proposed fuzzy stochastic approach is suitable to compromise a solution among the conflicting tangible and intangible attributes under the group decision-making process. In addition, an application of the proposed approach is provided to solve the important risk selection problem in a highway project. The results illustrate its real-life applicability and suitability in discrete group decision-making under uncertainty.

An MAGDM Approach Combining Numerical Values with Uncertain Linguistic Information and Its Application in Evaluation of R&D Projects

In multi-attribute group decision making (MAGDM) problems, decision attributes of alternatives are often considered as with quantitative and qualitative information. Therefore decision making problems may include preference information in different formats. In this paper, a multi-attribute group decision making model based on numerical and uncertain linguistic information is investigated. Uncertain linguistic information reflects the subjectivity and uncertainty of evaluation with respect to qualitative attributes. A transformation function and an extended TOPSIS procedure are proposed to deal with this MAGDM model with numerical and uncertain linguistic information. In order to implement the TOPSIS procedure, the distance of interval numbers and the distance of n-dimensional interval numbers are defined, they are both in form of interval numbers to preserve the uncertainty of original information. The proposed approach is illustrated by a numerical example, and is applied in the evaluation of R&D projects in the final part of the paper.