Linguistic Multiple Attribute Group Decision Research Articles

CODAS method for Pythagorean 2-tuple linguistic multiple attribute group decision making

The evaluation of the financial performance of universities is conducive to the sustainable development of universities. Based on this, we extend the traditional CODAS (COmbinative Distance-based ASsessment) method to the Pythagorean 2-tuple linguistic fuzzy environment and propose a P2TL-CODAS model to evaluate the financial management performance of universities. Firstly, we briefly describe the definition, the score function, accuracy function, operational laws and the distance calculating method of P2TLSs. Next, two aggregation operators of P2TL including Pythagorean 2-tuple linguistic weighted averaging (P2TLWA) operator and Pythagorean 2-tuple linguistic weighted geometric (P2TLWG) operator are also introduced to fuse overall Pythagorean 2-tuple linguistic evaluation information. Then the steps of CODAS method are depicted briefly. Moreover, we use linguistic Pythagorean 2-tuple linguistic fuzzy numbers to extend the CODAS method. The P2TL-CODAS model is established and all computing steps are simply presented. Furthermore, we apply the proposed method to evaluate the financial management performance about five universities. Finally, a comparison between P2TL-CODAS method and P2TL-TODIM method is made to demonstrate the stability of the new method. The results show that the proposed method has unique advantages.

Retracted: Grey Relational Analysis Method for Probabilistic Double Hierarchy Linguistic Multiple Attribute Group Decision Making and Its Application to College Tennis Classroom Teaching Effect Evaluation

Retracted: Grey Relational Analysis Method for Probabilistic Double Hierarchy Linguistic Multiple Attribute Group Decision Making and Its Application to College Tennis Classroom Teaching Effect Evaluation

Grey Relational Analysis Method for Probabilistic Double Hierarchy Linguistic Multiple Attribute Group Decision Making and Its Application to College Tennis Classroom Teaching Effect Evaluation

The college tennis classroom teaching effect evaluation is viewed as the multiattribute group decision making (MAGDM). The probabilistic double hierarchy linguistic term set (PDHLTS) not only conforms to people’s language expression habit of “adverb + adjective” but also can accurately depict its importance in real MAGDM. Therefore, this paper comes up with the probabilistic double hierarchy linguistic grey relational analysis (PDHL-GRA) method based on the grey relational analysis (GRA) process for MAGDM based on PDHLTS environment and applies it to the college tennis classroom teaching effect evaluation. Finally, a practical case for college tennis classroom teaching effect evaluation is presented to demonstrate the steps of our method, and a comparison analysis illustrates its feasibility and effectiveness.

Supplier selection with different risk preferences and attribute sets: An innovative study based on generalized linguistic term sets

Selecting the optimal supplier is crucial to the management of the company’s supply chain, which has received widespread attention from academia and business circles. Generally, a variety of suppliers and multiple attributes are usually involved in this selection proceeding which can be comparatively regarded as a linguistic multiple attribute group decision making (MAGDM) problem. However, common linguistic MAGDM problems may not take the following characteristics into consideration. Due to the limited knowledge, distinctive interests, and different semantic value expectations of decision makers (DMs), it is necessary for them to consider their diverse risk preferences and use multi-granular linguistic term sets (LTSs) to assess suppliers on individual attribute sets independently. Meanwhile, the complex decision environment may have influences on the integrity of the attribute weight information, such that it is always incompletely known. To deal with the afore-mentioned situations, this paper presents a procedure based on risk preferences and several attribute sets with incomplete weight information for choosing the desirable supplier. Firstly, a new multi-granular fuzzy linguistic transformation model is constructed to normalize linguistic domains of multi-granular generalized linguistic term sets (GLTSs). Multi-granular GLTSs are introduced to describe semantic values of multi-granular LTSs given by DMs with risk preferences. Secondly, according to the maximizing deviation method with incomplete attribute weight information, an optimization model is also established to determine attribute weight vectors of individual attribute sets. Thirdly, a novel method that comprises of the aforesaid models is presented to handle supplier selection problems with risk preferences and several attribute sets. Finally, an illustrative example on supplier selection and comparative analyses are provided to clarify the validity and feasibility of our proposed method. Significantly, the initiation of the proposed method and its application could afford to the theoretical development of linguistic MAGDM, as well as the practical expansion in the domain of supplier selection.

EDAS method for probabilistic linguistic multiple attribute group decision making and their application to green supplier selection

EDAS method for probabilistic linguistic multiple attribute group decision making and their application to green supplier selection

Probabilistic linguistic multiple attribute group decision making for location planning of electric vehicle charging stations based on the generalized Dice similarity measures

The location of the electric vehicle charging station is deemed to be a multiple attribute group decision making (MAGDM) issue involving many experts and many conflicting attributes. In practical MAGDM issues, the information of uncertain and fuzzy cognitive decision is well-depicted by linguistic term sets (LTSs). These LTSs could be simply shifted into the probabilistic linguistic sets (PLTSs). In such paper, we design some novel probabilistic linguistic weighted Dice similarity measures (PLWDSM) and the probabilistic linguistic weighted generalized Dice similarity measures (PLWGDSM). Subsequently, the PLWGDSM-based MAGDM methods are presented under PLTSs. In the end, a practical case which concerns about the location planning of electric vehicle charging stations is offered to demonstrate the proposed PLWGDSM’s applicability and advantages.

GRA method for waste incineration plants location problem with probabilistic linguistic multiple attribute group decision making

In this paper, we provide the probabilistic linguistic multiple attribute group decision making (PL-MAGDM) with incomplete weight information. In such method, the linguistic information firstly is shifted into probabilistic linguistic information. For obtaining the weight information of the attribute, two optimization models are built on the basis of the basic idea of grey relational analysis (GRA), by which the attribute weights can be obtained. Then, the optimal alternative is obtained through calculating largest relative relational degree from the probabilistic linguistic positive ideal solution (PLPIS) which considers both the largest grey relational coefficient (GRC) from the PLPIS and the smallest GRC form probabilistic linguistic negative ideal solution (PLNIS). Finally, a case study for waste incineration plants location problem is given to demonstrate the advantages of the developed methods.

Model for selection of hospital constructions with probabilistic linguistic GRP method

Probabilistic linguistic term sets are used to express uncertain decision information in multiple attribute group decision making problems. For probabilistic linguistic multiple attribute group decision making (MAGDM) with weight determined by CRITIC (Criteria Importance Through Intercriteria Correlation) method, the probabilistic linguistic grey relational projection method is proposed in this paper. Firstly, the correlation coefficient among attributes and standard deviation of each attribute are utilized to compute the attributes weights. Then the most ideal alternative is decided by means of counting the grey relational projection (GRP) from probabilistic linguistic positive ideal solution and probabilistic linguistic negative ideal solution. In the end, a numerical example for site selection of hospital constructions is applied to further account for the extended method. The result demonstrates the availability of the proposed method and it can be used in other fields which refers to problems of selection.

Algorithms for probabilistic uncertain linguistic multiple attribute group decision making based on the GRA and CRITIC method: application to location planning of electric vehicle charging stations

Electric vehicles (EVs) could be regarded as one of the most innovative and high technologies all over the world to cope with the fossil fuel energy resource crisis and environmental pollution issues. As the initiatory task of EV charging station (EVCS) construction, site selection play an important part throughout the whole life cycle, which is deemed to be multiple attribute group decision making (MAGDM) problem involving many experts and many conflicting attributes. In this paper, a grey relational analysis (GRA) method is investigated to tackle the probabilistic uncertain linguistic MAGDM in which the attribute weights are completely unknown information. Firstly, the definition of the expected value is then employed to objectively derive the attribute weights based on the CRiteria Importance Through Intercriteria Correlation (CRITIC) method. Then, the optimal alternative is chosen by calculating largest relative relational degree from the probabilistic uncertain linguistic positive ideal solution (PULPIS) which considers both the largest grey relational coefficient from the PULPIS and the smallest grey relational coefficient from the probabilistic uncertain linguistic negative ideal solution (PULNIS). Finally, a numerical case for site selection of electric vehicle charging stations (EVCS) is designed to illustrate the proposed method. The result shows the approach is simple, effective and easy to calculate.

Pythagorean 2-Tuple Linguistic Taxonomy Method for Supplier Selection in Medical Instrument Industries.

Supplier selection in medical instrument industries is a classical multiple attribute group decision making (MAGDM) problem. The Pythagorean 2-tuple linguistic sets (P2TLSs) can reflect uncertain or fuzzy information well and solve the supplier selection in medical instrument industries, and the original Taxonomy is very appropriate for comparing different alternatives with respect to their advantages from studied attributes. In this study, we present an algorithm that combines Pythagorean 2-tuple linguistic numbers (P2TLNs) with the Taxonomy method, where P2TLNs are applied to express the evaluation of decision makers on alternatives. Relying on the Pythagorean 2-tuple linguistic weighted average (P2TLWA) operator or Pythagorean 2-tuple linguistic weighted geometric (P2TLWG) operator to fuse P2TLNs, the new general framework is established for Pythagorean 2-tuple linguistic multiple attribute group decision making (MAGDM) under the classical Taxonomy method. Ultimately, an application case for supplier selection in medical instrument industries is designed to test the novel method’s applicability and practicality and a comparative analysis with three other methods is used to elaborate further.

TODIM method for probabilistic linguistic multiple attribute group decision making based on the similarity measures and entropy

TODIM method for probabilistic linguistic multiple attribute group decision making based on the similarity measures and entropy

GRA Method for Probabilistic Linguistic Multiple Attribute Group Decision Making with Incomplete Weight Information and Its Application to Waste Incineration Plants Location Problem

In this essay, we investigate the probabilistic linguistic multiple attribute group decision making (PL-MAGDM) with incomplete weight information. In this method, the linguistic representation developed recently is converted into probabilistic linguistic information. For deriving the weight information of the attribute, an optimization model is built on the basis of the fundamental idea of grey relational analysis (GRA), by which the attribute weights can be decided. Then, the optimal alternative is chosen through calculating largest relative relational degree from the probabilistic linguistic positive ideal solution (PLPIS) which considers both the largest grey relational coefficient (GRC) from the PLPIS and the smallest GRC form probabilistic linguistic negative ideal solution (PLNIS). In the end, a case study concerning waste incineration plants location problem is given to demonstrate the merits of the developed methods.

Some new Hamacher aggregation operators under single-valued neutrosophic 2-tuple linguistic environment and their applications to multi-attribute group decision making

Abstract This paper proposes an approach to linguistic multiple attribute group decision making (MAGDM) problem with single-valued neutrosophic 2-tuple linguistic (SVN2TL) assessment information by adding a subjective imprecise estimation of reliability of the 2-tuple linguistic terms (2TLTs). SVN2TL includes the truth-membership (TM), indeterminacy-membership (IM) and faulty-membership (FM), which can express the incomplete, indeterminate and inconsistent information perfectly and avoid information and precision losing in aggregation process ideally. We first propose the concept of SVN2TL set (SVN2TLS) and single valued neutrosophic 2-tuple linguistic element (SVN2TLE), basic operational rules on SVN2TLEs via Hamacher triangular norms, and ranking method for SVN2TLEs. Then, some SVN2TL aggregation operators including SVN2TL Hamacher weighted averaging (SVN2TLHWA) operator, SVN2TL Hamacher geometric weighted averaging (SVN2TLHGWA) operator, are developed, their some properties are investigated as well. Moreover, we apply new operators to develop approach to MAGDM problem with SVN2TL assessment information, where a model for optimal weighting vector is constructed. Finally, an numerical example related to evaluation of emergency response solutions for sustainable community development is provided to show the utility and effectiveness of the method described in this paper. A sensitivity and comparative analysis are also conducted to demonstrate the strength and practicality of the proposed method.

Multiple attribute group decision making based on generalized trapezoid fuzzy linguistic prioritized weighted average operator

In this paper, we investigate the trapezoid fuzzy linguistic multiple attribute group decision making problem with priority considerations among attributes as well as decision makers. By combining the idea of generalized mean and prioritized weighted average operator, we propose a new prioritized weighted aggregation operator called generalized trapezoid fuzzy linguistic prioritized weighted average (GTFLPWA) operator for aggregating trapezoid fuzzy linguistic information. Properties and special cases of the new aggregation operator are studied in detail. Furthermore, using GTFLPWA operator, an approach to deal with multiple attribute group decision-making problems under trapezoid fuzzy linguistic environments is developed. Finally, a practical example is illustrated to show the feasibility and superiority of the proposed approach.

Model for evaluating the virtual enterprise’s risk with 2-tuple linguistic information

With the increasingly fierce competition in the market, the virtual enterprises are becoming a realistic choice of enterprises. As a new business mode, the virtual enterprises bring tremendous opportunities to improve the competitiveness of enterprises to a certain degree. However, without enough practical and proper theoretical experience, the failure probability of virtual enterprises is high. The virtual enterprises have certain advantages, but the complexity of their organization makes the uncertainties increasing, the risk analysis control is difficult and the risk management is complex. In order to achieve smooth operation and expected profits, the virtual enterprises must succeed to avoid the risks. Therefore, the risk management for virtual enterprises is significant in theoretical and practical research. In this paper, we investigate the 2-tuple linguistic multiple attribute group decision making problems with incomplete weight information, some basic concepts and operational laws of 2-tuple linguistic variables are introduced. A model based on the entropy weight method, by which the attribute weights can be determined, is established. According to the traditional ideas of grey relational analysis (GRA), the optimal alternative(s) is determined by calculating the linguistic degree of grey relation of every alternative and 2-tuple linguistic positive ideal solution and 2-tuple linguistic negative ideal solution. It is based on the concept that the optimal alternative should have the largest degree of grey relation from positive ideal solution and the smallest degree of grey relation from the negative ideal solution. Finally, an illustrative example for evaluating the virtual enterprise's risk is given to verify the developed approach and to demonstrate its practicality and effectiveness.

A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

The aim of this paper is to put forward a consensus reaching method for multi-attribute group decision-making (MAGDM) problems with linguistic information, in which the weight information of experts and attributes is unknown. First, some basic concepts and operational laws of 2-tuple linguistic label are introduced. Then, a grey relational analysis method and a maximising deviation method are proposed to calculate the incomplete weight information of experts and attributes respectively. To eliminate the conflict in the group, a weight-updating model is employed to derive the weights of experts based on their contribution to the consensus reaching process. After conflict elimination, the final group preference can be obtained which will give the ranking of the alternatives. The model can effectively avoid information distortion which is occurred regularly in the linguistic information processing. Finally, an illustrative example is given to illustrate the application of the proposed method and comparative analysis with the existing methods are offered to show the advantages of the proposed method.

Some prioritized aggregating operators with linguistic information and their application to multiple attribute group decision making

In this paper, we investigate the linguistic multiple attribute group decision making MADM problems in which the attributes and experts are in different priority level. Motivated by the ideal of prioritized aggregation operators R.R. Yager, Prioritized aggregation operators, International Journal of Approximate Reasoning 482008 263-274., we develop some prioritized aggregation operators for aggregating linguistic information, and then apply them to develop some models for linguistic multiple attribute group decision making MAGDM problems in which the attributes and experts are in different priority level. Finally, a practical example about talent introduction is given to verify the developed approach and to demonstrate its practicality and effectiveness.

UNCERTAIN LINGUISTIC PRIORITIZED AGGREGATION OPERATORS AND THEIR APPLICATION TO MULTIPLE ATTRIBUTE GROUP DECISION MAKING

In this paper, we investigate the uncertain linguistic multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Motivated by the idea of prioritized aggregation operators (R. R. Yager, Prioritized aggregation operators, Int. J. Approximate Reasoning48 (2008) 263–274.), we develop some prioritized aggregation operators for aggregating uncertain linguistic information, and then apply them to develops some models for uncertain linguistic multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Finally, a practical example about talent introduction is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Uncertain Linguistic Multiple Attribute Group Decision Making Approach and Its Application to Software Project Selection

This paper proposes a new approach to solve the multiple attribute group decision making (MAGDM) problem where attribute values are in the format of uncertain linguistic information and attribute weights are in the format of linguistic information. Firstly, concepts and c omparison laws of uncertain 2-tuple are given. Then, an uncertain 2-tuple ordered weighted averaging (UTOWA) operator is used to calculate alternative appraisal values with respect to all attributes for each decision maker (DM) and to aggregate all DMs’ preferences into a collective opinion. Furthermore, ranking alternatives or selecting the most desirable alternative(s) is conducted according to the comparison laws. Finally, a numerical example on software project selection is used to illustrate the applicability and effectiveness of the proposed approach.

Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

AbstractWith respect to 2-tuple linguistic multiple attribute group decision making problems with incomplete weight information, some basic concepts and operational laws of 2-tuple linguistic variables are introduced. An optimization model based on the maximizing deviation method, by which the attribute weights can be determined, is established. According to the traditional ideas of grey relational analysis (GRA), the optimal alternative(s) is determined by calculating the linguistic degree of grey relation of every alternative and 2-tuple linguistic positive ideal solution and 2-tuple linguistic negative ideal solution. It is based on the concept that the optimal alternative should have the largest degree of grey relation from positive ideal solution and the smallest degree of grey relation from the negative ideal solution. The method has exact characteristic in linguistic information processing. It avoided information distortion and losing which occur formerly in the linguistic information processing. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easy to calculate.