Intuitionistic fuzzy MULTIMOORA approach for multi-criteria assessment of the energy storage technologies

Prioritization of renewable energy for offshore ship charging stations based on intuitionistic fuzzy GLDS method: A case of China

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts' fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

Partitioned dual Maclaurin symmetric mean operators based on picture fuzzy sets and their applications in multi-attribute decision-making problems

The partitioned Dual Maclaurin symmetric mean (PDMSM) operator has the supremacy that can justify the interrelationship of distinct characteristics and there are a lot of exploration consequences for it. However, it has not been employed to manage "multi-attribute decision-making" (MADM) problems represented by picture fuzzy numbers. The basic inspiration of this identification is to develop the novel theory of picture fuzzy PDMSM operator, and weighted picture fuzzy PDMSM operator and to identify their important results (Idempotency, Monotonicity, and Boundedness). Further, to identify the best decision, every expert realized that they needed the best way to find the beneficial optimal using the proper decision-making procedure, for this, we diagnosed the MADM tool in the consideration of deliberated approaches based on PF information. Finally, to drive the characteristics of the invented work, several examples are utilized to test the manifest of the comparative analysis with various more existing theories, which is a fascinating and meaningful technique to deeply explain the features and exhibited of the proposed approaches.

IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks

Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. Moreover, the deep learning based AI is employed to interpret the alarming patterns into real faults by which the system minimizes the human based fault recognition errors. The Sensors Information Modeling (SIM) and the Internet of Things (IoT) have the potential to improve the efficiency of industrial production machines maintenance management. This research work provides a better maintenance strategy by utilizing a data-driven predictive maintenance planning framework based on our proposed SIM and IoT technologies. To verify the feasibility of our approach, the proposed framework is applied practically on a corrugated cardboard production factory in real industrial environment. The Fuzzy Logic System (FLS) is utilized to achieve the AI based PM while the Deep Learning (DL) is applied for the alarming and fault diagnosis in case the fault already occured.

A novel extension of Pythagorean fuzzy MULTIMOORA approach for new product development

This study explores how to enhance the decision-making processes in the phases of idea generation or alternative selection during the new product development (NPD) process. NPD is acknowledged as the central function of businesses in an increasingly competitive marketplace. In the current era, the highly uncertain and rapidly changing market environment makes the NPD extremely vague and complex. To be able to find a suitable solution to this complexity, the proposed research aims to categorize the decision points in the process of NPD regarding software development and identify the fuzziness elements affecting the process. The goal of a decision-making process is to prioritize several alternatives with respect to some required objectives and to select the best among them. Multi-Criteria Decision-Making (MCDM) may support reaching a consensus judgment with the collective assessment of Decision Makers (DMs). We introduce a novel evaluation approach for this problem. The proposed approach employs a MULTIMOORA (Multi-objective Optimization by Ratio Analysis plus the Full Multiplicative Form) MCDM technique under Pythagorean Fuzzy Sets (PFSs) objective world environment to cope with an ambiguous environment using Group Decision Making (GDM) setting to shape the decisions. The PFSs have demonstrated their advantages in dealing with vagueness and uncertainty over crisp, fuzzy, or intuitionistic fuzzy sets. Therefore, PFSs can represent the DMs' judgments and preferences in a better structure, ensuring enhanced decision-making in a group consensus. A case study on gaming software and app development is presented to validate the functionality of the proposed method. The results are compared and assessed with the help of a sensitivity analysis. This research makes a real contribution to the literature by proposing a novel evaluation method to rate and select NPD (gaming software and apps) to deal with the inexactness and vagueness associated with the criteria and alternatives.

Evaluation of dynamic technological innovation capability in high-tech enterprises based on pythagorean fuzzy LBWA and MULTIMOORA

With the rapid development of science and technology, high-tech enterprises need to constantly carry out technological innovation to adapt to the changes in the external environment, and maintain their competitive advantages. However, the current research on technological innovation of high-tech enterprises is carried out from a static perspective, which is difficult to understand the dynamic evolution process of continuous technological innovation of high-tech enterprises in a turbulent environment. Therefore, this paper studies high-tech enterprises’ dynamic technological innovation ability from a dynamic perspective, through literature reading and the investigation of the technological innovation status of high-tech enterprises, the evaluation index system of 12 indicators under three dimensions is constructed. The multi-objective optimization by ratio analysis plus full multiplicative form (MULTIMOORA) –Level-based weight assessment (LBWA) comprehensive evaluation model based on Pythagorean fuzzy number (PFN) is proposed to evaluate the dynamic technological innovation ability of high-tech enterprises. Finally, the accuracy and reliability of the model are verified by case analysis. The result of this study shows that the ability to identify new technological knowledge and information outside the enterprise, the ability to obtain technological innovation resources, and the ability to strengthen the input of innovation resources are important factors for the dynamic technological innovation capability of enterprises, so enterprises should pay more attention from these aspects. This study provides a new comprehensive evaluation model and evaluation results can help the decision-makers find their strengths and weaknesses in time and improve them, to promote the sustainable development of high-tech enterprises.

A Combined Weighting Based Large Scale Group Decision Making Framework for MOOC Group Recommendation

Massive open online courses (MOOC) are free learning courses based on online platforms for higher education, which not only promote the open sharing of learning resources, but also lead to serious information overload. However, there are many courses on MOOCs, and it can be difficult for users to choose courses that match their individual or group preferences. Therefore, a combined weighting based large-scale group decision-making approach is proposed to implement MOOC group recommendations. First, based on the MOOC operation mode, we decompose the course content into three stages, namely pre-class, in-class, and post-class, and then the curriculum-arrangement-movement- performance evaluation framework is constructed. Second, the probabilistic linguistic criteria importance through intercriteria correlation method is employed to obtain the objective weighting of the criterion. Meanwhile, the word embedding model is utilized to vectorize online reviews, and the subjective weighting of the criteria are acquired by calculating the text similarity. The combined weighting then can be obtained by fusing the subjective and objective weighting. Based on this, the PL-MULTIMIIRA approach and Borda rule is employed to rank the alternatives for group recommendation, and an easy-to-use formula for group satisfaction is proposed to evaluate the effect of the proposed method. Furthermore, a case study is conducted to group recommendations for statistical MOOCs. Finally, the robustness and effectiveness of the proposed approach were verified through sensitivity analysis as well as comparative analysis.

A new approach for spherical fuzzy TOPSIS and spherical fuzzy VIKOR applied to the evaluation of hydrogen storage systems

This study proposes a new perspective of the TOPSIS and VIKOR methods using the recently introduced spherical fuzzy sets (SFSs) to handle the vagueness in subjective data and the uncertainties in objective data simultaneously. When implementing these techniques using SFSs, two main problems might arise that can lead to incorrect results. Firstly, the reference points might change with the utilized score function. Secondly, the distance between reference points might not be the largest, as known, among the available ratings. To overcome these deficiencies and increase the robustness of these two methods, they are implemented without utilizing any reference points to minimize the effect of defuzzification and without measuring the distance to eliminate the effect of distance formulas. In the proposed methods, when using an SFS to express the performance of an alternative for a criterion, this SFS per se can be viewed as a measure of proximity to the aspired level. On the other hand, the conjugate of the SFS can be viewed as a measure of proximity to the ineffectual level. Two practical applications are presented to demonstrate the proposed techniques. The first example handles a warehouse location selection problem. The second example evaluates hydrogen storage systems for automobiles with different types of data (crisp, linguistic variables, type 1 fuzzy sets). These data are transformed to SFSs to provide a more comprehensive analysis. A comparative study is conducted with earlier versions of TOPSIS and VIKOR to explicate the adequacy of the proposed methods and the consistency of the results.

Distance measure and intuitionistic fuzzy TOPSIS method based on the centroid coordinate representation *

In real life, people often need to aggregate some multi criteria fuzzy information and then make reasonable and effective decisions. The distance measure in intuitionistic fuzzy set (IFS) space is an important tool to deal with multi criteria information fuzzy decision making problems. Motivated by these reasons, an intuitionistic fuzzy TOPSIS multi criteria decision-making method is proposed based on distance measure represented by centroid coordinates. Firstly, some existing distance measures in IFS space are summarized, and some of existing shortcomings are discussed. Secondly, the concept of hesitation factor is proposed by using the centroid coordinate representation of hesitation region, and then a new distance measure between two intuitionistic fuzzy numbers is defined. It is proved that the distance measure satisfies the traditional distance axioms. Then, an intuitionistic fuzzy TOPSIS method based on the proposed distance measure is developed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method. Also, the superiority and advantages of the method are shown via comparative analysis and discussion.

A simple combined projection method for conservative decision-making

Machine learning and artificial intelligence based techniques have brought great convenience to human life but along with a series of algorithmic “black box”, discrimination and ethical issues. One of the solutions is to integrate human and machine like the expert evaluation based research of multi-attribute decision-making where “human brain intelligence” is used for the support of “artificial intelligence”. In this article, we proposed a new and effective method to evaluate and rank alternatives in multi-attribute decision-making. Different from many existing approaches, this proposed method employs both the projection lengths and the projection angles of alternatives to make decisions. It supports psychological desirableness of decision makers and uses a Relu function to further enhance the output qualities. This proposed method is very simple to construct and applicable for much wider situations than the existing similar methods.

The development of China's Circular Economy: From the perspective of environmental regulation

While achieving rapid economic growth, the pressure caused by environmental pollution and resource depletion has increasingly become a bottleneck in China's economic development, making the development of a circular economy particularly important. The extant literature has not focused on the influence of environmental regulation on a circular economy performance. This study uses the metafrontier global direction distance function (Metafrontier-Global-DDF) super-efficiency data envelopment analysis (DEA) model to estimate the circular economy performance and decomposition values of circular economy growth rate in 286 prefecture-level cities in China from 2003 to 2018. It further tests the influences of environmental regulations on circular economy performance and its influencing mechanism. The results show that environmental regulation can play a linear role in promoting the performance of the circular economy, mainly through the "catch-up effect," while "innovation effect" and "demonstration effect" have not yet played an effective role. This study provides evidence for the performance evaluation of the circular economy in China and the relationship between environmental regulations and circular economy performance. The future development of a circular economy still needs the active development of circular economy technology in each city. The role of the "innovation effect" and "demonstration effect" in improving the performance of the circular economy should be further enhanced.

VIKOR method for intuitionistic fuzzy multi-attribute group decision-making and its application to teaching quality evaluation of college English

The results of the nation-wide public college English tests and some other English tests held by the college itself are very valuable data for the assessment of the English teaching level. In the light of the selected and processed data and some other auxiliary approaches, the writer suggests that a modernized monitoring system be established to improve English teaching quality. Under this system, teachers’ and students’ English level can be evaluated in an objective way. Consequently, the subsequent measures such as teachers’ promotion as well as students’ development and employment and the evaluation of the quality of test-papers can be taken scientifically. We can solve the above issues with help of multi-attribute group decision making (MAGDM) method. Depending on the VIKOR steps and given intuitionistic fuzzy sets (IFSs), this paper devises the IF-VIKOR to assess the teaching quality of college English. In addition, the weights of attribute are derived through CRITIC method. Then, the VIKOR method is extended to IFSs to derive the order of each alternative. Therefore, all alternatives could be ranked and the best one can be identified. Eventually, we give an example of college English teaching quality evaluation, according to some comparison with other methods to make an analysis, the results show that the method proposed in such paper is effective and easy to compute.

An Intuitionistic Extension of the Simple WISP Method

In this article, we present a new extension of the Integrated Simple Weighted Sum-Product (WISP) method, adapted for intuitionistic numbers. The extension takes advantage of intuitionistic fuzzy sets for solving complex decision-making problems. The example of contractor selection demonstrates the use of the proposed extension.

An extended MAIRCA method using intuitionistic fuzzy sets for coronavirus vaccine selection in the age of COVID-19

All over the world, the COVID-19 outbreak seriously affects life, whereas numerous people have infected and passed away. To control the spread of it and to protect people, appreciable vaccine development efforts continue with increasing momentum. Given that this pandemic will be in our lives for a long time, it is obvious that a reliable and useful framework is needed to choose among coronavirus vaccines. To this end, this paper proposes a new intuitionistic fuzzy extension of MAIRCA framework, named intuitionistic fuzzy MAIRCA (IF-MAIRCA) to assess coronavirus vaccines according to some evaluation criteria. Based on the group decision-making, the IF-MAIRCA framework both extracts the criteria weights and discovers the prioritization of the alternatives under uncertainty. In this work, as a case study, five coronavirus vaccines approved by the world's leading authorities are evaluated according to various criteria. The findings demonstrate that the most significant criteria considered in coronavirus vaccine selection are “duration of protection,” “effectiveness of the vaccine,” “success against the mutations,” and “logistics,” respectively, whereas the best coronavirus vaccine is AZD1222. Apart from this, the proposed model's robustness is verified with a three-phase sensitivity analysis.

A novel integrated AHP and MULTIMOORA method with interval-valued spherical fuzzy sets and single-valued spherical fuzzy sets to prioritize financial ratios for financial accounting fraud detection

Many studies have used different financial ratios for financial accounting fraud detection. This study focuses on multicriteria decision-making (MCDM) for ranking 25 financial ratios with respect to six criteria in detecting financial accounting fraud using interval-valued spherical fuzzy sets (IVSFS) and single-valued spherical fuzzy sets (SVSFS) to overcome uncertainties in decision-making process of financial analysts. This study proposes an integrated Analytic Hierarchy Process (AHP) and Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form (MULTIMOORA) approach using IVSFS and SVSFS. Comparative results are obtained and discussed in prioritization of financial ratios for both IVSFS and SVSFS.

Fuzzy-Based Investigation of Challenges for the Deployment of Renewable Energy Power Generation

Studying and analyzing the challenges that the renewable energy sector faces can help evaluate the risks and improve the planning. This research is done by considering the challenges in the implementation of sustainable generation of electricity through RESs in India, based on factors, including technical, financial, involvement, support, and others. The triangular fuzzy number (TFN) method, based on fuzzy logic concept, is used to analyze the challenges in this study. In general, TFN comprises of three numbers, likewise Gaussian fuzzy numbers, trapezoidal fuzzy numbers also exist. The classified sets of numbers are denotations to decision-makers’ perspective or a choice towards the criterion preference. Although alternatives are many to design a fuzzy set depending on elements count, the TFNs are the ones considered as actual representations of a fuzzy number. On the other hand, cases the Gaussian or trapezoidal, are manifestations of fuzzy intervals. Another argument is that the membership function shape corresponding to the number of fuzzy set elements is largely dependent on the study. The challenges identified along with analysis in this paper will help the industry, governments, and policymakers focus and tackle essential issues to facilitate further the deployment of RESs in India towards a more sustainable country.

A Multi-Criteria Decision-Making Approach for Energy Storage Technology Selection Based on Demand

Energy storage technologies can reduce grid fluctuations through peak shaving and valley filling and effectively solve the problems of renewable energy storage and consumption. The application of energy storage technologies is aimed at storing energy and supplying energy when needed according to the storage requirements. The existing research focuses on ranking technologies and selecting the best technologies, while ignoring storage requirements. Here, we propose a multi-criteria decision-making (MCDM) framework for selecting a suitable technology based on certain storage requirements. Specifically, we consider nine criteria in four aspects: technological, economic, environmental, and social. The interval number, crisp number, and linguist terms can be transformed into a probabilistic dual hesitant fuzzy set (PDHFS) through the transformation and fusion method we proposed, and a suitable technology can be selected through distance measurements. Subsequently, the proposed method is applied in a representative case study for energy storage technology selection in Shanxi Province, and a sensitivity analysis gives different scenarios for elaboration. The results show that the optimal selection of energy storage technology is different under different storage requirement scenarios. The decision-making model presented herein is considered to be versatile and adjustable, and thus, it can help decision makers to select a suitable energy storage technology based on the requirements of any given use case.

Selection of new energy vehicles using hybrid approach: A case study of China

With resource shortage and environmental pollution becoming more and more serious, the development of new energy vehicles (NEVs) plays an important role. In this paper, the hybrid method of best-worst method (BWM), Multi-Objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), and Evaluation based on Distance from Average Solution (EDAS) is used to evaluate new energy vehicles (NEVs) and select the best new energy vehicle. BWM method is used to obtain the subjective preference weight, MULTIMOORA method is used to integrate the objective data with the subjective weight to evaluate new energy vehicles, and the final ranking of alternatives is obtained by the EDAS method. The paper collect the data of 22 representative new energy vehicle types in China, the validity and feasibility of the method is verified.

Selection of third party reverses logistic providers: an approach of BCF-CRITIC-MULTIMOORA using Archimedean power aggregation operators

One of the most powerful tools to operate imprecision is bipolar complex fuzzy sets (BCFSs), which is an enlargement of bipolar fuzzy sets (BFSs) as well as complex fuzzy sets (CFSs). This paper deals with an integrated MULTIMOORA (multi-objective optimization on the basis of ratio analysis plus full multiplicative form) framework as a generalization of fuzzy MULTIMOORA procedure to assess the multi-criteria decision-making (MCDM) problems with BCFSs. We develop BCF-Archimedean power weighted (ordered weighted) arithmetic and geometric aggregation operators (AOs) and discuss their properties from this point of view. The proposed Archimedean power-weighted AOs can eliminate the influence of extreme evaluating criteria values from some biased experts with different preference attitudes under the BCF setting. Afterward, we put forward an integrated MULTIMOORA algorithm based on the proposed AOs, where criteria weights are estimated using the CRITIC (criteria importance through inter-criteria correlation) method, which is a well-known objective weighting method based on aggregated score values of options, intensity contrast of every criteria and conflict among attributes. In the proposed methodology, criteria values are aggregated based on the MULTIMOORA method that involves three sub-methods: the ‘ratio system’, the ‘reference point’ and the ‘full multiplicative form’ and thus takes less computational time, minimum mathematical evaluations and bears good stability. In the following, third-party reverse logistics providers' (3PRLP) selection problem is brought into consideration to manifest the sufficiency of the developed methodology. At the end of this study, we draw attention to a comparison between the proposed decision-making approach with the corresponding BCF-CRITIC-TOPSIS and BCF-CRITIC-WASPAS methods.

An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

Off-office audit of natural resource assets and water pollution: a quasi-natural experiment in China

Purpose

The purpose of this paper is to analyze the impact of the off-office audit of natural resource assets on the prevention and control of water pollution against a background of big data using a differences-in-differences model.

Design/methodology/approach

This study constructs a differences-in-differences model to evaluate the policy effects of off-office audit based on panel data from 11 cities in Anhui Province, China, from 2011 to 2017, and analyzes the dynamic effect of the audit and intermediary effect of industrial structure.

Findings

The implementation of the audit system can effectively reduce water pollution. Dynamic effect analysis showed that the audit policy can not only improve the quality of water resources but can also have a cumulative effect over time. That is, the prevention and control effect on water pollution is getting stronger and stronger. The results of the robustness test verified the effectiveness of water pollution prevention and control. However, the results of the influence mechanism analysis showed that the mediating effect of the industrial structure was not obvious in the short term.

Practical implications

These findings shed light on the effect of the off-office audit of natural resource assets on the prevention and control of water pollution, and provide a theoretical basis for the formulation of relevant environmental policies. Furthermore, these findings show that the implementation of the audit system can effectively reduce water pollution, which has practical significance for the sustainable development of China's economy against the background of big data.

Originality/value

This study quantitatively analyzes the policy effect of off-office auditing from the perspective of water resources based on a big data background, which differs from the existing research that mainly focuses on basic theoretical analysis.

Algorithms for MAGDM with intuitionistic fuzzy sets and their application for evaluating the green technological innovation ability of the enterprises

The dual generalized Bonferroni mean (DGBM) operator is a meaningful decision-making tool which can consider the relationship between any numbers of being fused arguments and has been applied to many MAGDM domains in past few years. The intuitionistic fuzzy sets (IFSs), which is characterized by the functions of membership degree and non-membership degree, has been investigated by numerous scholars. In this manuscript, combine the DGBM operator and IFSs, the major contribution and objective of the work is to develop two new aggregation operators: the dual generalized intuitionistic fuzzy BM (DGIFBM) operator and the dual generalized intuitionistic fuzzy weighted BM (DGIFWBM) operator. The last, we give an application example for evaluating the green technological innovation ability of the enterprises and some comparative analysis to testify the effective and scientific of our developed methods.

Cubic q-Rung Orthopair Fuzzy Heronian Mean Operators and Their Applications to Multi-Attribute Group Decision Making

The cubic q-rung orthopair fuzzy set (Cq-ROFS) contains much more information to determine the interval valued q-rung orthopair fuzzy sets (IVq-ROFSs) and q-rung orthopair fuzzy sets (q-ROFSs) simultaneously for coping with the vagueness in information. It provides more space for decision makers (DMs) to describe their opinion in the environment of fuzzy set (FS) theory. In this paper, firstly, we introduce the conception of Cq-ROFS and their characteristics. Further, the Heronian mean (HM) operator based on Cq-ROFS, called the weighted HM operator, are explored. To overcome the deficiency of HM operator and keeping in mind the partitioned structure in real decision situations, we offer Cubic q-rung orthopair fuzzy partitioned HM operator and its weighted shape. An algorithm of the proposed operators based on multi-attribute group decision making (MAGDM) problems for the selection of best alternative among the given ones is established. Lastly, we provide an example to depict the authenticity and advantages of the exposed methods by contrasting with other existing drawbacks.

Visibility Graph Power Geometric Aggregation Operator and Its Application in Water, Energy, and Food Efficiency Evaluation

In the real decision-making process, there are so many time series values that need to be aggregated. In this paper, a visibility graph power geometric (VGPG) aggregation operator is developed, which is based on the complex network and power geometric operator. Time series data are converted into a visibility graph. A visibility matrix is developed to denote the links among different time series values. A new support function based on the distance of two values are proposed to measure the support degree of each other when the two time series values have visibility. The VGPG operator considers not only the relationship but also the similarity degree between two values. Meanwhile, some properties of the VGPG operator are also investigated. Finally, a case study for water, energy, and food coupling efficiency evaluation in China is illustrated to show the effectiveness of the proposed operator. Comparative analysis with the existing research is also offered to show the advantages of the proposed method.

Single-Valued Neutrosophic Linguistic Logarithmic Weighted Distance Measures and Their Application to Supplier Selection of Fresh Aquatic Products

A single-valued neutrosophic linguistic set (SVNLS) is a popular fuzzy tool for describing deviation information in uncertain complex situations. The aim of this paper is to study some logarithmic distance measures and study their usefulness in multiple attribute group decision making (MAGDM) problems within single-valued neutrosophic linguistic (SVNL) environments. For achieving the purpose, SVNL weighted logarithmic averaging distance (SVNLWLAD) and SVNL ordered weighted logarithmic averaging distance (SVNLOWLAD) measures are firstly developed based on the logarithmic aggregation method. Then, the SVNL combined weighted logarithmic averaging distance (SVNLCWLAD) measure is presented by unifying the advantages of the previous SVNLWLAD and SVNLOWLAD measures. Moreover, a new MAGDM model by utilizing the SVNLCWLAD measure is presented under SVNL environments. Finally, a supplier selection for fresh aquatic products is taken as a case to illustrate the performance of the proposed framework.

A Probabilistic Linguistic Multiple Attribute Decision Making Based on a New Correlation Coefficient Method and its Application in Hospital Assessment

The probabilistic linguistic term set (PLTS) is a newly emerging mathematical tool for handling uncertainties. It is considered a useful extension of linguistic term sets associated with probability information and can improve the effectiveness of multiple attribute decision making (MADM). This paper proposes a new PLTS correlation coefficient and addresses its usefulness in MADM problems. For achieving this aim, some new concepts of mean, variance, and covariance of the PLTS are first proposed. Moreover, a novel PLTS Pearson correlation coefficient is defined to overcome the shortcomings of the existing methods, whose significant feature is that it lies in the interval [−1,1], which makes it more effective in reflecting the negative and positive correlation between PLTSs. A weighted PLTS Pearson correlation coefficient is further defined to consider the importance of attribute weights and expand the scope of application. Then, a relative PLTS closeness coefficient is constructed based on the developed Pearson correlation coefficient, and based on which, a Pearson correlation-based TOPSIS (technique for order of preference by similarity to ideal solution) approach for MADM problems is developed. Finally, the effectiveness as well as the applicability of the developed method are illustrated through numerical examples and comparative analysis.

Fuzzy Decision Support Modeling for Hydrogen Power Plant Selection Based on Single Valued Neutrosophic Sine Trigonometric Aggregation Operators

In recent decades, there has been a massive growth towards the prime interest of the hydrogen energy industry in automobile transportation fuel. Hydrogen is the most plentiful component and a perfect carrier of energy. Generally, evaluating a suitable hydrogen power plant site is a complex selection of multi-criteria decision-making (MCDM) problem concerning proper location assessment based on numerous essential criteria, the decision-makers expert opinion, and other qualitative/quantitative aspects. This paper presents the novel single-valued neutrosophic (SVN) multi-attribute decision-making method to help decision-makers choose the optimal hydrogen power plant site. At first, novel operating laws based on sine trigonometric function for single-valued neutrosophic sets (SVNSs) are introduced. The well-known sine trigonometry function preserves the periodicity and symmetric in nature about the origin, and therefore it satisfies the decision-maker preferences over the multi-time phase parameters. In conjunction with these properties and laws, we define several new aggregation operators (AOs), called SVN weighted averaging and geometric operators, to aggregate SVNSs. Subsequently, on the basis of the proposed AOs, we introduce decision-making technique for addressing multi-attribute decision-making (MADM) problems and provide a numerical illustration of the hydrogen power plant selection problem for validation. A detailed comparative analysis, including a sensitivity analysis, was carried out to improve the understanding and clarity of the proposed methodologies in view of the existing literature on MADM problems.

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.