Multiple Attribute Decision Making Problems Research Articles

Complex Pythagorean Normal Interval-Valued Fuzzy Aggregation Operators for Solving Medical Diagnosis Problem

This paper presents a new methodology for solving multiple-attribute decision-making problems (MADMs) using a complex Pythagorean normal interval-valued fuzzy set (CPNIVFS), which is an extended concept of a complex Pythagorean fuzzy set. Four types of different aggregating operations (AOs), including CPNIVF weighted averaging (CPNIVFWA), CPNIVF weighted geometric (CPNIVFWG), generalized CPNIVFWA (CGPNIVFWA), and generalized CPNIVFWG (CGPNIVFWG), are discussed. The scoring function, accuracy function, and operational laws of the CPNIVFS are defined. Algebraic structures, such as associative, distributive, idempotent, bounded, commutativity, and monotonicity properties, are also shown to be satisfied by complex Pythagorean normal interval-valued fuzzy numbers. Furthermore, an algorithm is proposed to solve the MADM problems based on the defined AOs. The proposed approach is then used for a medical diagnosis problem about brain tumors because computer science and machine tool technology are among the most important components of brain tumor research. The five types of brain tumors diagnosed in these patients are gliomas, meningiomas, metastases, embryonal tumors, and ependymomas. Several types of treatments are available, which are often combined as part of an overall treatment plan. Brain tumors can be treated in various ways, including surgery, radiation therapy, chemotherapy, immunotherapy, and clinical trials. Based on the comparisons and options gathered, the most suitable treatment can be chosen. In this regard, it is evident that the value of the integer ⅁\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Game $$\\end{document} plays a significant role in determining the model. The candidate models under consideration can be validated by comparing them with the previously proposed ones. The proposed technique is compared with the existing method to demonstrate its superiority and validity, and the results conclude that the former is more reliable and effective than the latter. Finally, the criteria are evaluated by expert assessments to determine the most appropriate options.

Modified TODIM-TOPSIS technique for type-2 neutrosophic number multiple-attribute decision-making and applications to innovation and entrepreneurship education evaluation in vocational colleges

Innovation and entrepreneurship education is an important component of cultivating the comprehensive quality of college students and an important force in promoting economic and social development. Meanwhile, due to changes in the social environment and economic structure, traditional university education is no longer able to meet the needs of contemporary society. Therefore, innovation and reform of innovation and entrepreneurship education for college students are urgent. Innovation and entrepreneurship education for college students needs to keep up with the times, constantly update concepts and techniques, in order to adapt to the ever-changing social and economic environment. The innovation and entrepreneurship education evaluation in the application-oriented vocational colleges is a multiple-attribute decision-making (MADM) problem. Recently, the TODIM and TOPSIS technique has been used to cope with MADM issues. The Type-2 neutrosophic numbers (T2NNs) are employed as a technique for characterizing uncertain information during the innovation and entrepreneurship education evaluation in the application-oriented vocational colleges. In this paper, the Type-2 neutrosophic number TODIM-TOPSIS (T2NN-TODIM-TOPSIS) technique is implemented to solve the MADM under T2NNs. Finally, a numerical case study for innovation and entrepreneurship education evaluation in the application-oriented vocational colleges and several comparative analysis is implemented to validate the proposed T2NN-TODIM-TOPSIS technique. The main research contribution of this paper is managed: (1) the TODIM and TOPSIS technique was enhanced with T2NNs; (2) Entropy technique is enhanced to manage the weight values with T2NNs. (3) the T2NN-TODIM-TOPSIS technique is founded to manage the MADM with T2NNs; (4) Algorithm framework for innovation and entrepreneurship education evaluation in the application-oriented vocational colleges and several comparative analysis are constructed based on one numerical example to verify the effectiveness of the T2NN-TODIM-TOPSIS technique.

Research on the urban green transportation development level evaluation based on the triangular pythagorean fuzzy multiple attribute decision making

With the acceleration of urbanization and the significant improvement of people’s living standards, the motorization of urban transportation in China has developed rapidly, and the number of urban motor vehicles has sharply increased. This has also caused a series of problems such as increasingly severe urban road traffic congestion, increased traffic energy consumption, and atmospheric environmental pollution. Unprecedented social and environmental pressures have put forward higher requirements for the development model of urban transportation. Against the backdrop of increasingly severe conflicts between urban transportation and resource environment in China, green transportation with the goal of “meeting maximum demand with minimum consumption” has gradually received widespread attention from the academic community. The urban green transportation development level evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional geometric BM (GBM) operator and generalized weighted GBM (GWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy generalized GBM (TPFGGBM) operator and triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for urban green transportation development level evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity.

Research on effectiveness evaluation of corporate culture construction based on the neutrosophic cubic number multiple attribute decision making

Corporate culture has its own development laws and may play a significant role in a short period of time, but its development and improvement are a relatively long-term task. The construction of corporate culture is a systematic project that varies depending on the enterprise. For enterprises, the construction of corporate culture has very important practical significance for the re integration of the enterprise team and the enhancement of competitive strength. Culture is a productive force, and the role of corporate culture is more direct. Therefore, enterprises should focus on their own development goals, create their own unique corporate culture based on learning and reference, and meet market challenges with a new look and strong strength. The effectiveness evaluation of corporate culture construction is a classical multiple attribute decision making (MADM). Recently, the TODIM and VIKOR method has been used to cope with MADM issues. The neutrosophic cubic sets (NCSs) are used as a tool for characterizing uncertain information during the effectiveness evaluation of corporate culture construction. In this manuscript, the neutrosophic cubic number TODIM-VIKOR (NCN-TODIM-VIKOR) method is built to solve the MADM under NCSs. In the end, a numerical case study for effectiveness evaluation of corporate culture construction is given to validate the proposed method. The research aim of the paper is summarized: (1) the NCN-TODIM-VIKOR is proposed for MADM problem with NCSs; (2) The attributes weight information is obtained through information entropy; (3) the NCN-TODIM-VIKOR method is designed for effectiveness evaluation of corporate culture construction and were compared with some existing methods; (4) Through the comparison, it is found that NCN-TODIM-VIKOR method for effectiveness evaluation of corporate culture construction proposed are effective.

An intelligent decision methodology for triangular Pythagorean fuzzy MADM and applications to college English teaching quality evaluation

Abstract The development of contemporary college English teaching methods and practice should respect the subjective will of college students; fully tap their wisdom and potential; constantly innovate college English teaching methods, teaching practice activities, classroom teaching, and teaching evaluation models; create a good teaching atmosphere; and establish the concept of developmental evaluation. Through the innovation of teaching methods, college students’ actual language communication ability is cultivated, the teaching objectives and teaching methods of college English reading are innovated, and interactive, body language, role play, situational teaching, communicative teaching, and other teaching methods are used. The College English teaching quality evaluation is a classical multiple attribute decision making (MADM). In this article, the triangular Pythagorean fuzzy sets (TPFSs) are introduced, and the MADM problem is investigated under TPFSs. Based on the traditional dual generalized weighted Bonferroni mean operator and power average operator, the triangular Pythagorean fuzzy dual generalized power Bonferroni mean (TPFDGPBM) operator is proposed. Accordingly, the TPFDGPBM operator is employed to construct the triangular Pythagorean fuzzy MADM method. Finally, a numerical example for College English teaching quality evaluation is constructed to verify the TPFDGPBM technique.

MADM framework based on interval-valued neutrosophic aczel-alsina power average operator and application to translation teaching quality evaluation of college English

The huge demand for translation talents has prompted the universities to focus on improving the quality of translation teaching. As a necessary means to improve the quality of teaching, translation teaching quality evaluation plays a role in guiding, regulating and managing translation teaching, which is an important part of translation teaching. The translation teaching quality evaluation of college English is viewed as the multiple attribute decision making (MADM). The interval-valued neutrosophic sets (IVNSs) are used as an effective tool for characterizing uncertain information during the translation teaching quality evaluation of college English. In this paper, interval-valued neutrosophic number (IVNN) Aczel-Alsina power average (IVNNAAPA) operator is proposed based on the classical Aczel-Alsina operator and power average (PA) operator. Then, IVNNAAPA operator is employed to cope with MADM problem. Finally, the numerical decision example for translation teaching quality evaluation of college English is employed to illustrate the produced method and some comparative models are used to prove the effectiveness of the IVNNAAPA method.

A new approach to MADM problems with belief distributions based on weighted similarity measure and regret theory

In multiple attribute decision making (MADM) problems, when belief distributions (BDs) are used to express the preference information of decision-maker, obtaining the attribute weights using the difference among the assessments is a complex but interesting challenge. This paper introduces the weighted similarity measure of belief distribution to distinguish the alternatives to the greatest extent. After proposing the concepts of similarity measure and weighted similarity measure for two belief distribution vectors, an optimization model is developed to generate the attribute weights. After that, the comparison rules and the ideal belief distributions are defined, then the regret theory is extended to rank alternatives with the consideration of the psychological behavior of the decision-maker. The main characteristic of the proposed method involves: (1) An optimization model based on weighted similarity measure is proposed to derive attribute weights in BD environment; (2) The psychological behavior is taken into account to assist the decision-maker in avoiding regret; Finally, the applicability and validity of the proposed method are demonstrated by a medical example in which doctors could make the diagnosis with minimal regret.

Selection of Optimal Approach for Cardiovascular Disease Diagnosis under Complex Intuitionistic Fuzzy Dynamic Environment

Cardiovascular disease (CVD) is a leading global health concern. There is a critical need for accurate and reliable decision-making tools to select the optimal approach for diagnosing cardiovascular disease (CVD). In this study, we have addressed this pressing issue. Complex intuitionistic fuzzy set (CIFS) theory is adept at encapsulating vagueness due to its capability to encompass comprehensive problem specifications characterized by both intuitionistic uncertainty and periodicity. Within the scope of this article, we present two novel aggregation operators: the complex intuitionistic fuzzy dynamic weighted averaging (CIFDWA) operator and the complex intuitionistic fuzzy dynamic weighted geometric (CIFDWG) operator. Some intriguing characteristics of these operators are elucidated, and important special cases are also defined in detail. We devise an enhanced score function to rectify the deficiencies observed in the existing score function under complex intuitionistic fuzzy knowledge. Furthermore, these operators are employed in the development of a systematic approach for the handling of multiple attribute decision-making (MADM) scenarios involving complex intuitionistic fuzzy data. Moreover, we undertake the resolution of an MADM problem, wherein we ascertain the optimal approach for diagnosing cardiovascular disease (CVD) through the utilization of the proposed operators, thereby substantiating their utility in decision-making processes. Finally, we conduct a comprehensive comparative analysis, pitting the presented operators against an array of existing counterparts, in order to demonstrate the reliability and stability inherent in the derived methodologies.

A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation

. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets.

A novel multi-interval-valued fuzzy set model to solve MADM problems

This paper introduces a new Multi-interval-valued fuzzy sets (MIVFSs) model, in which the membership degrees of an alternative to given a set are represented by multiple interval values within the [0, 1] range along with their respective counting numbers. Then, the related concepts, order strategies, and some basic operational rules of multi-interval-valued fuzzy elements are investigated. Afterwards, several information measures including distance, similarity, and interval projection are further developed. Building upon this foundation, we designed two multiple-attribute decision-making (MADM) ranking methods in the multi-interval-valued fuzzy information system. To illustrate the practical application of our model, we provide a numerical example involving linguistic information, addressing the issue of ranking doctors in online healthcare. Through a comparative analysis, we demonstrate the effectiveness and practicality of the proposed model in handling MADM problems.

A Supplier Selection Decision-Making Approach for Complex Product Development Based on Hesitant Fuzzy Information

During the development process of complex products, selecting the best desirable alternative supplier is a challenge since an improperly selected alternative may cause losing capacity and increasing the cycle time and cost of development for a company. For this multiple-attribute decision-making problem of supplier selection, in this paper, a supplier selection problem in which the decision data are hesitant fuzzy information and the attribute weight is unknown in complex product development is investigated, and a supplier selection decision-making approach based on hesitant fuzzy information is proposed. Firstly, a bidirectional projection based on hesitant fuzzy information is established, and then the measurement equation for the degree of closeness is improved. Further, an attribute weight determination model which minimizes the projection total deviation for the hesitant fuzzy elements is constructed. By solving this model, scientific and reasonable attribute weights are provided. Subsequently, an illustrative example is employed to not only give the ranking result of alternative suppliers but also demonstrate the validity and feasibility of the developed approach. Meanwhile, sensitivity analysis and comparative analysis are put forward to illustrate the stability of the given final ranking result and the advantages and reliability of the constructed method. For alternative or strategy selection, this proposed approach can be used as a decision-making means when uncertainties are inherent.

A Modified TOPSIS Approach with Three-Way Decision

In real life, multiple attribute decision problems (MADM) can be applied in different areas and numerous related extensions and methodologies have been proposed by researchers. Combining three-way TOPSIS decision ideas with MADM is a feasible and meaningful research direction. In light of this, this paper generalizes the classical TOPSIS method with the help of mean and standard deviation and proposes the so-called modified three-way TOPSIS. First, using a pair of thresholds which is derived by mean and standard deviation, we divide decision alternatives into three segments, and then a preliminary rank results of decision alternatives can be obtained. Furthermore, in each decision region, we use two ranking regulations (one-way TOPSIS or modified two-way TOPSIS method) to rank decision alternatives. A practical example of urban expressway route selection illustrates the feasibility of the proposed method. Finally, we test the feasibility and validity of the modified three-way TOPSIS method by comparing with some existing method.

MADM framework based on the triangular Pythagorean fuzzy sets and applications to college public English teaching quality evaluation

The so-called “college English” teaching quality evaluation is to provide a basic, comprehensive, and realistic evaluation of the relevant aspects and management of teaching implementation on the basis of following the general laws of higher education; It is a comprehensive inspection of “College English” teaching and an important means of quality monitoring and policy adjustment for “College English”. As mentioned earlier, teaching evaluation is a comprehensive evaluation of teaching. Therefore, our evaluation of the quality of university public education is actually an examination of our specific measures in evaluating teaching, teaching methods and methods, teaching literature, and other aspects. The college public English teaching quality evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional dual generalized weighted Bonferroni mean (DGWBM) operator and dual generalized weighted geometric Bonferroni mean (DGWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy DGWBM (TPFDGWBM) operator and triangular Pythagorean fuzzy DGWGBM (TPFDGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for college public English teaching quality evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity.

A decision support method for designing the blended teaching effectiveness evaluation of English courses in universities based on interval-valued neutrosophic information

The goal of internationalization, the actual needs of the new generation of college students, and the innovation of information technology have led to a shift from teaching English for general purposes (EGP) to teaching English courses. The blended teaching effectiveness evaluation of English courses in universities in an output-oriented perspective is viewed as the multiple attribute decision making (MADM). In this paper, Taxonomy method is designed for MADM under interval neutrosophic sets (INSs). Then, the interval neutrosophic numbers Taxonomy (INN-Taxonomy) method is formed to cope with MADM problem. Finally, a numerical decision example for blended teaching effectiveness evaluation of English courses in universities in an output-oriented perspective is given to demonstrate the INN-Taxonomy method.

Q-rung orthopair normal fuzzy Maclaurin symmetric mean aggregation operators based on Schweizer-Skla operations

In order to be able to make a good decision, we need to evaluate the uncertainty information of multiple attributes of different alternatives that obey the normal distribution, and the interrelationship among multiple attributes should be considered in the process of evaluating. This paper aims to propose a new multiple attribute decision-making (MADM) method, which uses a new aggregation operator to evaluate the uncertainty information that obey normal distribution comprehensively. We firstly extended Schweizer-Sklar (SS) t-norm (TN) and t-conorm (TCN) to q-rung orthopair normal fuzzy number (q-RONFN) and defined the Schweizer-Skla operational laws of q-rung orthopair normal fuzzy set (q-RONFs). Secondly, we developed q-rung orthopair normal fuzzy Maclaurin symmetric mean aggregation operators based on SS operations considering that the Maclaurin symmetric mean operator can reflect the interrelationship among multiple input variables. Furthermore, we discussed some desirable properties of the above operators, such as monotonicity, commutativity, and idempotency. Lastly, we proposed a novel MADM method based on developed aggregation operators. A numerical example on enterprise partner selection is given to testify the effectiveness of the developed method. The results of analysis indicated that our proposed aggregation operators have stronger information aggregation ability and are more general and flexible for MADM problems.

Developing sustainable management strategies in construction and demolition wastes using a q-rung orthopair probabilistic hesitant fuzzy set-based decision modelling approach

Sustainable management of construction and demolition wastes (CDWs) has become a pressing global issue in social, environmental and economic contexts, and it involves complex technological, engineering, management and regulatory challenges. Recently, many CDW management strategies have been developed based on the barrier attributes of reuse distribution. However, no strategy can simultaneously address all barrier attributes of reuse distribution. Furthermore, no research has assessed and modelled the identified CDW management strategies to determine optimality. On this basis, the presence of multiple barrier attributes, varying attribute priority and a wide range of data allow for the modelling of CDW management strategies under complex multiple-attribute decision-making (MADM) problems. This study develops the fuzzy-weighted zero inconsistency (FWZIC) and fuzzy decision by opinion score method (FDOSM)-based multiplicative multiple objective optimisation by ratio analysis (MULTIMOORA) with the q-rung orthopair probabilistic hesitant fuzzy set (q-ROPHFS) to address this problem. The developed q-ROPHFS–FWZIC method prioritised and weighted the main and sub-barrier attributes of reuse distribution in CDW management strategies. The developed q-ROPHFS–FDOSM is used to score the CDW management strategies. Then, the MULTIMOORA method is used to model 51 CDW management strategies to determine the optimum one. Results showed that Strategy 46 modelled first in six q values because it had the most essential attributes (i.e. cost, market, value-for-money, experience, infrastructure, management, risk and trust). Strategy 17 and Strategy 20 are the least sustainable strategies because they had only one attribute (i.e. experience). Sensitivity analysis, systematic modelling and comparison analysis are conducted to validate and evaluate the stability and robustness of the proposed methods. The implications of this study would likely benefit various stakeholders involved in the construction industry, including construction companies, architects, engineers, policy-makers and members of the public.

Robotic sensor based on score and accuracy values in [formula omitted]-rung complex diophatine neutrosophic normal set with an aggregation operation

The multiple-attribute decision-making (MADM) problem is resolved through the q-rung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (q-rung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology. Four factors are to evaluate when determining a sensor robotics system’s quality: resolution, sensitivity, error, and environment. It is possible to compare expert opinions with the criteria and determine the best alternative. Therefore, the value of q significantly impacts the model’s results. To prove that the models considered are valid and useful, we will compare the current and proposed models. Thus, q has a significant impact on the results of the model.

Normal [formula omitted] functions-based fuzzy information measures with applications in MADM and pattern recognition

Fuzzy and non-standard fuzzy information measures act upon vague information entailed in linguistic variables or uncertain numerical data following some goal for which they discover the “best alternative” or “correct pattern”. This paper introduces the concept of normalKN functions instrumental to generate a class of fuzzy knowledge measures. In addition to the axiomatic approach, we investigate various general methods to obtain normalKN functions from the notions of auto-morphisms, restricted equivalence functions, and restricted dissimilarity functions. Further, aggregatingnormal KNfunctions, we propose three knowledge measures of a fuzzy set and exploit their significance in practical applications. This concerns Multiple Attribute Decision Making (MADM) problems, the issue of appropriate weight computation, and the understanding of structured linguistic variables. Additionally, we consider the real case study for the ranking of universities. A novel methodology to transform a given fuzzy knowledge measure into a family of similarity measures of fuzzy sets is also suggested. Furthermore, we discuss an application of the proposed similarity measures in the context of pattern recognition and the MADM problem using artificial and real data sets. The comparative study of the proposed measures shows their advantage over existing ones because of specific theoretical and practical aspects.

Multiple-Attribute Decision Making Based on the Probabilistic Dominance Relationship with Fuzzy Algebras

In multiple-attribute decision-making (MADM) problems, ranking the alternatives is an important step for making the best decision. Intuitionistic fuzzy numbers (IFNs) are a powerful tool for expressing uncertainty and vagueness in MADM problems. However, existing ranking methods for IFNs do not consider the probabilistic dominance relationship between alternatives, which can lead to inconsistent and inaccurate rankings. In this paper, we propose a new ranking method for IFNs based on the probabilistic dominance relationship and fuzzy algebras. The proposed method is able to handle incomplete and uncertain information and can generate consistent and accurate rankings.

Intelligent decision methodology for business English teaching quality evaluation based on GHM and PG operators with 2-tuple linguistic neutrosophic numbers

Business English teaching quality evaluation Business English is a new type of composite specialty, which is a discipline innovation made by China’s higher education to adapt to the new market demand and international standards since the reform and opening up. Over the past 20 years, it has cultivated a number of compound talents for the cause of China’s reform and opening up. However, the backwardness of business English theoretical research has greatly restricted the development of business English. At present, Business English has been officially approved as a new major for undergraduate enrollment by the Ministry of Education of the People’s Republic of China. Its subject nature, specialty structure, training objectives, and specialty compound characteristics need to be qualitatively studied theoretically. The business English teaching quality evaluation is viewed as the multiple attribute decision making (MADM) issue. In this paper, we connect the geometric Heronian mean (GHM) operator and power geometric (PG) with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose the generalized 2-tuple linguistic neutrosophic power geometric HM (G2TLNPGHM) operator. Then, the G2TLNGHM operator is applied to deal with the MADM problems under 2TLNNs. Finally, an example for business English teaching quality evaluation is used to show the proposed methods. Some comparative analysis and parameter influence analysis are fully given. The results show that the built algorithms method is useful for business English teaching quality evaluation.