This paper aims to present a new multi-attribute group decision-making (MAGDM) approach for solving problems in an uncertain and complex environment. In any MAGDM problem, the key challenge is how to quantify and aggregate the objective uncertainty information contained in the data. To answer this, in this paper, we utilize the idea of linguistic Pythagorean fuzzy set (LP y FS) to describe the information in terms of linguistic membership degree (LMD) and linguistic non-membership degree (LNMD). We first present some new operations of LP y FS, including Aczel–Alsina (AA) sum, AA product, and AA scalar multiplication. At that point, we develop various LP y F aggregation operators which are based on these defined operations, including the LP y F Aczel–Alsina weighted averaging (LP y FAAWA) operator, the LP y F Aczel–Alsina ordered weighted averaging (LP y FAAOWA) operator, LP y F Aczel–Alsina weighted geometric (LP y FAAWG) operator and LP y F Aczel–Alsina ordered weighted geometric (LP y FAAOWG) operator. We configure these operators with different characteristics. It can be seen that the proposed operators have commutativity, monotonicity, boundary, and idempotency properties. Then, we design a new technique dependent on these operators to solve MAGDM problems. The applicability of the established approach is demonstrated through a case study related to smart city development. The result demonstrates the new technique's feasibility and applicability. In the end, sensitivity analysis and a systematic comparison between the prevailing and suggested techniques are given.

In this study, the concept of linguistic Z-number fuzzy soft set ( L Z n F S S) is proposed to describe multiple uncertainties in practical decision making problems. L Z n F S S combines the concepts of fuzzy soft set, linguistic Z-number, and soft set, which could reflect both of the uncertainty in structure and the uncertainty in detailed evaluations. As an initial idea, the set operations on L Z n F S S s are put forward, the properties of such operations are also discussed. With traditional soft set based decision procedure and fuzzy soft set based decision procedure, a novel linguistic Z-number fuzzy soft set based group decision procedure is developed to solve multiattribute group decision making with linguistic Z-numbers. Wherein an extended technique for order preference by similarity to ideal solution is also developed. Finally, a numerical example is shown to illustrate the practicality and effectiveness of the given method.

Hesitant fuzzy linguistic preference relation (HFLPR) is a new preference structure that the decision makers (DMs) are hesitant about several possible linguistic terms of preference information for pairwise comparison between alternatives. This paper examines the additive consistency of HFLPR with a new expansion principle for hesitant fuzzy linguistic term sets (HFLTSs). In order to normalize HFLTSs with different numbers of linguistic terms, a least common multiple expansion (LCME) principle is proposed. According to the LCME principle, the additive consistent index of an HFLPR is defined to measure the consistency level of the HFLPR. For improving the unacceptable additive consistency of an HFLPR, a pure integer programming model is constructed to derive an acceptable additive consistent HFLPR. In group decision making (GDM) with HFLPRs, the similarities between DMs are calculated based on their individual HFLPR with acceptable additive consistency. Subsequently, the confidence degrees of DMs are defined to derive DMs’ weights, and some examples including an investment project management problem are analyzed to verify the effectiveness of the proposed method.

We develop a novel computation model of unbalanced linguistic variables on the basis of Archimedean copulas and corresponding co-copulas, which provides a new tool to aggregate unbalanced linguistic information. The properties of the proposed computational model are also studied. We present the concepts of weighted unbalanced Archimedean copula arithmetic aggregation operators and weighted unbalanced Archimedean copula geometric aggregation operators. The properties of these aggregation operators are further investigated. Finally, a group decision making of sensory evaluation is introduced to illustrate the feasibility and validity of our proposed computational model.

The aim of this paper is to develop some closed algebra operational laws for interval linguistic labels based on extended t-norms and s-norms. We discuss the properties of these operational laws, such as commutative law, associative law, and distribution law. Different kinds of extended t-norms and s-norms, such as the extended algebraic, extended Einstein, extended Hamacher, and extended Frank t-norms and s-norms, have been investigated to produce different operational laws. As an application of such operational laws, we propose some extended t-norms and s-norms based interval linguistic weighted power average operators. We also study some basic properties of such aggregation functions. The cross-entropy of interval linguistic information is proposed and applied to obtain the weights of attributes. An approach to multiple attributes decision making with interval linguistic information is proposed. Finally, two cases of practical decision issues are illustrated to show the application of the proposed method.

We propose two new approaches to rank all the alternatives in the group decision making (GDM) with interval multiplicative preference relation (IMPR) and point out their equivalence on solutions, based on the continuous ordered weighted geometric (COWG) operator. First we introduced the COWG operator and used it to aggregate the individual IMPR into crisp one, which can be obtained the priority vector by eigenvector method. Then we develop two new approaches to derive the group priority vector. One is the weighted geometric mean method (WGMM) of all individual priority vectors under the condition of α - acceptable consistency of multiplicative preference relation. The other is the relative entropy method (REM), in which an optimization model is constructed to minimize the difference between the group priority vector and all individual priority vectors based on the conception of relative entropy. We find that the REM has the same solution with WGMM, which provides the optimal theory basis of WGMM. Finally, a numerical example is illustrated to rank the given alternatives in the GDM with IMPR and show that the REM is feasible.

The existing group consensus models for solving the group decision making (GDM) problems with [0,1] reciprocal preference relations are always on the basis of absolute deviation measures. In this paper, we define the individual consistency accuracy index and the group consensus accuracy index based on the relative deviation measure, which can measure the relative precision of difference among the group members. Then, two algorithms are developed to design the decision support model, and a Group Consensus Accuracy Model (GCA Model) is presented to determine the importance weight of each preference relation by maximizing group consensus accuracy index. The main characteristics of our model are that: (1) it guarantees that the original preference information is most likely reserved in each round; (2) the weights of decision makers are obtained objectively by using the GCA Model with relative deviation. Finally, performance of the proposed method is illustrated by an example.

Linguistic group decision making (GDM) always focuses on balanced linguistic terms sets, which are uniform and symmetrical. However, in real life there exist GDM problems that need to be assessed by linguistic terms sets with non-symmetrical and non-uniform distributions. The purpose of this paper is to present an unbalanced linguistic generalized power ordered weighted averaging (ULGPOWA) operator for aggregating unbalanced linguistic variables in GDM. The main characteristic of ULGPOWA operator is that the relationship among linguistic variables being aggregated is taken into account by using a support measure for each argument from others. Then properties of new operator are developed and an illustrative example is given to demonstrate the detailed process of proposed method, and the validity is verified by sensitivity analysis for the new approach.

In this paper, we investigate the multiple attributes group decision making (MAGDM) problem with 2-tuple linguistic information. According to some closed operational laws of 2-tuple linguistic, some Algebra t-norm and s-norm based Heronian aggregation operators of 2-tuple linguistic information are put forward, the desired properties and the special cases where the parameters take different values are also discussed. Furthermore, a method of MAGDM under 2-tuple linguistic environment is proposed based on the Algebra t-norm and s-norm based 2-tuple linguistic Heronian mean operator or the Algebra t-norm and s-norm based 2-tuple linguistic weighted Heronian mean operator. Finally, a numerical example is presented to demonstrate the proposed method.

The geometric Bonferroni mean (GBM) can capture the interrelationships between input arguments, which is an important generalization of Bonferroni mean (BM). In this paper, we combine geometric Bonferroni mean (GBM) with the power geometric average (PGA) operator under intuitionistic fuzzy environment and present the intuitionistic fuzzy geometric power Bonferroni mean (IFPGBM) and the weighted intuitionistic fuzzy power geometric Bonferroni mean (WIFPGBM). The desirable properties of these new extensions of Bonferroni mean and their special cases are investigated. We list the detailed steps of multiple attribute group decision making with the developed IFPGBM or WIFPGBM, and give a comparison of the new extensions of Bonferroni mean by this paper with the corresponding existing intuitionistic fuzzy Bonferroni means. Finally, examples are illustrated to show the validity and feasibility of the new approaches.

In this paper, we develop the generalized linguistic weighted logarithm averaging (GLWLA) operator and the generalized linguistic ordered weighted logarithm averaging (GLOWLA) operator in the group decision making under the linguistic surrounding. Then some properties of the families of the GLOWLA operator by different weighting vector are investigated. Furthermore, we present the generalized linguistic ordered weighted hybrid logarithm averaging (GLOWHLA) operator, which extends the GLOWLA operator. We also construct a nonlinear goal programming model to determine GLOWHLA weights from observational linguistic variable values under partial weight information. Finally, a numerical example is given to illustrate the new approach to evaluating university faculty for tenure and promotion, which indicates the feasibility and effectiveness of the new approach.

The Bonferroni mean (BM) was originally presented by Bonferroni and had been generalized by many researchers for its capacity to capture the interrelationship between input arguments. Nevertheless, the existing intuitionistic fuzzy BMs only consider the effects of membership function or nonmembership function of different intuitionistic fuzzy sets (IFSs). As complements to the existing generalizations of BM under intuitionistic fuzzy environment, this paper also considers the interactions between the membership function and nonmembership function of different IFSs and develops the intuitionistic fuzzy interaction BM and the weighted intuitionistic fuzzy interaction BM. We investigate the properties of these new extensions of BM and discuss their special cases. Furthermore, the detailed steps of multiple attribute decision making with the presented operators under intuitionistic fuzzy environment are investigated and an example is illustrated to show the validity and feasibility of the new approach.

As a useful generalization of fuzzy sets, the hesitant fuzzy set is designed for situations in which it is difficult to determine the membership of an element to a set because of ambiguity between a few different values. In this paper, we define the ith-order polymerization degree function and propose a new ranking method to further compare different hesitant fuzzy sets. In order to obtain much more information in the process of group decision making, we combine the power average operator with the Bonferroni mean in hesitant fuzzy environments and develop the hesitant fuzzy power Bonferroni mean and the hesitant fuzzy power geometric Bonferroni mean. We investigate the desirable properties of these new hesitant fuzzy aggregation operators and discuss some special cases. The new aggregation operators are utilized to present techniques for hesitant fuzzy multiple attribute group decision making. Finally, a numerical example is provided to illustrate the effectiveness of the developed techniques.

The aim of this paper is to develop a new compatibility, which is very suitable to deal with group decision making (GDM) problems involving interval multiplicative preference relations, based on the continuous ordered weighted geometric averaging (COWGA) operator. First, we define some concepts of the compatibility degree and the compatibility index for the two interval multiplicative preference relations based on the COWGA operator. Then, we study some desirable properties of the compatibility index and investigate the relationship between each expert's interval multiplicative preference relation and the synthetic interval multiplicative preference relation. The prominent characteristic of the compatibility index based on the COWGA operator is that it can deal with the compatibility of all the arguments in two interval arguments considering the risk attitude of decision maker rather than the compatibility of the two simple points in intervals. Second, in order to determine the experts' weights in the GDM with the interval multiplicative preference relations, we propose an optimal model based on the criterion of minimizing the compatibility index. Finally, we give a numerical example to develop the new approach to GDM with interval multiplicative preference relations.

The aim of this work is to develop a new compatibility for the uncertain multiplicative linguistic preference relations and determine the optimal weights of decision makers (DMs), which are very suitable to deal with group decision making (GDM) problems involving uncertain multiplicative linguistic preference relations. First, the concepts of compatibility degree and compatibility index for the two uncertain multiplicative linguistic preference relations are proposed. Then we prove the property that the synthetic uncertain linguistic preference relation is of acceptable compatibility under the condition that uncertain multiplicative linguistic preference relations given by DMs are all of acceptable compatibility with a specific linguistic preference relation, which is the scientific basis of using the uncertain multiplicative linguistic preference relations in the GDM. Next, in order to determine the weights of decision makers, we construct an optimal model based on the criterion of minimizing the compatibility index. Finally, we develop an application of the optimal weights approach compared with the equal weights approach where we analyze a GDM regarding the selection of investment.

We develop some new cases of the induced continuous ordered weighted averaging (ICOWA) operator and study their desirable properties, which are very suitable to deal with group decision making (GDM) with interval fuzzy preference relations. First, we present the consensus indicator ICOWA (CI-ICOWA) operator which uses the consensus indicator of the interval fuzzy preference as the order inducing variable in the ICOWA operator. Then the concept of compatibility degree (CD) for two interval fuzzy preference relations is defined based on the continuous ordered weighted averaging (COWA) operator and the compatibility degree ICOWA (CD-ICOWA) operator is proposed which uses the CD as the order inducing variable in the ICOWA operator. Next, we investigate some desirable properties of the CD-ICOWA operator. Additionally, we construct an optimization model to obtain the weights of experts by minimizing the compatibility degree in the GDM. Finally, an illustrative numerical example is used to verify the developed approaches.

We present the generalized ordered weighted logarithm averaging (GOWLA) operator based on an optimal deviation model. It is a new aggregation operator that generalizes the ordered weighted geometric averaging (OWGA) operator. This operator adds to the OWGA operator an additional parameter. controlling the power to which the arguments are raised. We further generalize the GOWLA operator and obtain the generalized ordered weighted hybrid logarithm averaging (GOWHLA) operator. We next introduce a nonlinear objective programming model for determining GOWHLA weights and an approach to group decision making based on the GOWHLA operator. Finally, we present a numerical example to illustrate the new approach in human resource management problem. © 2010 Wiley Periodicals, Inc.