Some operators of intuitionistic uncertain 2-tuple linguistic variables and application to multi-attribute group decision making with heterogeneous relationship among attributes

Optimizing microbial strain selection for pyrethroid biodegradation in contaminated environments through a TOPSIS-based decision-making system

Improved and contemporary agriculture relies heavily on pesticides, yet some can be quite persistent and have a stable chemical composition, posing a significant threat to the ecology. Removing harmful effects is upon their degradability. Biodegradation must be emphasized to lower pesticide degradation costs, especially in the soil. Here, a decision-making system was used to determine the best microbial strain for the biodegradation of the pyrethroid-contaminated soil. In this system, the criteria chosen as: pH (C1), Temp (C2), RPM (C3), Conc. (C4), Degradation (%) (C5) and Time required for degradation(hrs) (C6); and five alternatives were Bacillus (A1), Acinetobacter (A2), Escherichia (A3), Pseudomonas (A4), and Fusarium (A5). The best alternative was selected by applying the TOPSIS (technique for order performance by similarity to ideal solution) method, which evaluates based on their closeness to the ideal solution and how well they meet specific requirements. Among all the specified criteria, Acinetobacter (A2) was the best and optimal based on the relative closeness value (( R i ∗ ) = 0.740 (A2) > 0.544 (A5) > 0.480 (A1) > 0.403 (A4) > 0.296 (A3)). However, the ranking of the other alternatives is also obtained in the order Fusarium (A5), Bacillus (A1), Pseudomonas (A4), Escherichia (A3). Hence this study suggests Acinetobacter is the best microbial strain for biodegradation of pyrethroids; while least preference should be given to Escherichia. Acinetobacter, versatile metabolic nature with various xenobiotic compounds' degradation ability, is gram-negative, aerobic, coccobacilli, nonmotile, and nonspore forming bacteria. Due to less study about Acinetobacter it is not in that much frame as the other microorganisms. Hence, considering the Acinetobacter strain for the biodegradation study will give more optimal results than the other microbial strains. Novelty of this study, the TOPSIS method is applied first time in selecting the best microbial strain for the biodegradation of pyrethroid-contaminated soil, considering this selection process as multi-criteria decision-making (MCDM) problem.

Research on teaching quality evaluation of Chinese medicine specialty in higher vocational colleges with probabilistic uncertain linguistic TODIM-VIKOR method

Traditional Chinese medicine is a complex discipline that needs to combine theory with practice under the background of the magnificent Chinese history and civilization. It is a subject that needs “lifelong” learning. Teachers should gradually change the dull and rigid teaching mode in the past and explore a scientific and effective teaching mode that conforms to the background of the current era. Applying the advantages of the Internet to organically integrate teaching modes such as flipped classroom, which can stimulate students’ learning interest, cultivate students’ thinking mode of traditional Chinese medicine and clinical problem-solving ability, and realize the common development of students’ ability and quality of traditional Chinese medicine. While improving the teaching effect of internal medicine of traditional Chinese medicine, this diversified teaching method will provide new ideas and methods for deepening the reform of traditional Chinese medicine teaching and lead the teaching of traditional Chinese medicine to a new level. The teaching quality evaluation of Chinese medicine specialty in higher vocational colleges is classical multiple-attribute group decision-making (MAGDM) issues. Recently, the TODIM and VIKOR method has been used to solve MAGDM issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information during the teaching quality evaluation of Chinese medicine specialty in higher vocational colleges. In this manuscript, we design the TODIM-VIKOR model to solve the MAGDM under PULTSs. In the end, a numerical case study for teaching quality evaluation of Chinese medicine specialty in higher vocational colleges is given to validate the proposed method.

Research on quality evaluation of industry-education integration for rural vocational education in the perspective of rural revitalization with PL-MACONT method

As one of the teaching models that promote the orderly development of vocational education in China, the integration of industry and education has been recognized by all sectors of society in China’s many years of practice. In recent years, with the strong advocacy of the education sector in China, its development speed has been rapidly improved. Rural vocational education in China has also actively implemented and innovated the teaching mode of integration of industry and education, which has trained more excellent talents for agricultural development in various regions. The quality evaluation of industry-education integration for rural vocational education in the perspective of rural revitalization is viewed as the multiple attribute group decision making (MAGDM). In this paper, the probabilistic linguistic Mixed Aggregation by Comprehensive Normalization Technique (PL-MACONT) method is built for MAGDM. At last, to verify the validity of the extended method, a numerical example to further account for quality evaluation of industry-education integration for rural vocational education in the perspective of rural revitalization is put into use.

Modified CPT-TODIM method for evaluating the development level of digital inclusive finance under probabilistic hesitant fuzzy environment

Unlike traditional finance, digital inclusive finance is committed to integrating digital technology with the financial industry to bring groups originally excluded from traditional finance back into formal financial services and provide financial services at reasonable prices and matching needs for all social classes. Digital inclusive finance can effectively reduce the financing costs of SMEs, improve the external financing environment of enterprises, and provide more convenient, equal and perfect financial services for enterprises by using technical support such as "big data + artificial intelligence". The development level of digital inclusive finance is a classical multiple attributes group decision making (MAGDM). The Probabilistic hesitant fuzzy sets (PHFSs), which utilize the possible values and its possible membership degrees to depict decision-makers' behavior in different conditions, has been paid great attention. Though numerous methods have been applied in this environment since PHFSs has been introduced, there are still new fields to be explored. In this paper, we introduce the Cumulative Prospect Theory TODIM (CPT-TODIM) for probabilistic hesitant fuzzy MAGDM(PHF-MAGDM). Meanwhile, the information of entropy is utilized to calculate the weight of attributes, which is used to improve the classical TODIM method. At last, we utilize a numerical case for evaluating the development level of digital inclusive finance to compare the extended CPT-TODIM method with the classical TODIM method.

Algorithms for multiple attribute group decision making with intuitionistic 2-tuple linguistic information and its application

Recently, the TODIM has been used to deal with multiple attribute decision making (MADM) problems. The intuitionistic 2-tuple linguistic sets (I2TLSs) are useful tools to depict uncertainty in MADM. This paper use TODIM method to solve MADM with the intuitionistic 2-tuple linguistic numbers (I2TLNs). Firstly, the concepts of I2TLNs are briefly presented, and classical TODIM for MADM are introduced. Then, the classical TODIM method is used to solve MADM with I2TLNs, and its important characteristic is to fully consider the decision makers’ bounded rationality which is a real action in MADM. And a comparative analysis is also given. Finally, a numerical example for service outsourcing providers selection is proposed.

Algorithms for multiple attribute decision making with dual hesitant Pythagorean fuzzy information and their application to supplier selection

In this paper, we investigate the multiple attribute decision making (MADM) problem based on the Hamacher aggregation operators and Choquet integral with dual Pythagorean hesitant fuzzy information. Then, motivated by the ideal of Hamacher operation and Choquet integral, we have developed some Hamacher correlated operators for aggregating dual hesitant Pythagorean fuzzy information. The prominent characteristic of these proposed operators is studied. Then, we have utilized these two operators to develop some approaches to solve the dual hesitant Pythagorean fuzzy MADM problems. Finally, a practical example for supplier selection in supply chain management is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Picture fuzzy MABAC method based on prospect theory for multiple attribute group decision making and its application to suppliers selection

In the garment manufacturing industry, purchasing management is an important link. The materials of making clothes often need high cost. In addition, customers put forward a request in the quality of clothes. Thus, choosing an optimal supplier is an essential part of job. Reaching cooperation with an optimal supplier not only can help garment manufacturer improve the quality of clothes but also is benefit to reduce the cost of producing. Most importantly, it can improve the competitiveness of manufacture enterprises. So, it is important for managers to find an optimal supplier and make a cooperation with it. In this paper, we analysis an issue about choosing an optimal supplier during four different suppliers. With analyzing this problem, we can introduce an extended method under picture fuzzy environment to evaluate and choose an optimal supplier. In this article, we describe some basic knowledges about picture fuzzy sets (PFSs) and picture fuzzy numbers (PFNs). Then, we introduce the extension of MABAC method which is on the basis of prospect theory (PT) with picture fuzzy numbers (PF-PT-MABAC) and utilize the PF-PT-MABAC model to evaluate different suppliers to choose an optimal supplier. Finally, we compare the result of PF-PT-MABAC with the result of traditional MABAC, PFWG operators and traditional TODIM method to test the efficiency of PF-PT-MABAC model.

Grey relational analysis method based on cumulative prospect theory for intuitionistic fuzzy multi-attribute group decision making

The Multi-attribute group decision making (MAGDM) problem is an interesting everyday problem full of complexity and ambiguity. As an extended form of fuzzy sets, intuitionistic fuzzy sets (IFSs) can provide decision-makers (DMs) with a wider range of preferences for MAGDM. The grey relational analysis (GRA) is an effective method for dealing with MAGDM problems. However, in view of the incomplete and asymmetric information and the influence of DMs’ psychological factors on the decision result, we develop a new model that GRA method based on cumulative prospect theory (CPT) under the intuitionistic fuzzy environment. Moreover, the weight of attribute is calculated by entropy weight, so as to distinguish the importance level of attributes, which greatly improves the credibility of the selected scheme. simultaneously, the proposed method is used to the selection of optimal green suppliers for testifying the availability of this new model and the final comparison between this new method and the existing methods further verify the reliability. In addition, the proposed method provides some references for other selection problems.

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.

Application of multi-attribute group decision-making methods in urban road traffic safety evaluation with interval-valued intuitionistic fuzzy information

With the development of the social economy, the level of motorization has been greatly improved, and the traffic safety problem has been paid more and more attention. In recent years, China’s road traffic accident rate showed a trend of decline after rising first, suggests that the Chinese road traffic safety level is on the decline. Road traffic safety evaluation has a positive effect in found risk factors of road traffic safety in time and reduce the traffic accident rate, so the study of traffic safety evaluation method is imperative. And the urban road traffic safety evaluation is frequently viewed as the multi-attribute group decision-making (MAGDM) problem. Depending on the conventional VIKOR method and interval-valued intuitionistic fuzzy sets (IVIFSs), this paper designs a novel IVIF-VIKOR method to assess the urban road traffic safety. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria is [Z1] decided objectively by utilizing CRITIC method. Eventually, an application and some comparative analysis are given. The results show that the designed algorithms are useful for assessing the urban road traffic safety.

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.

Green supplier selection in steel industry with intuitionistic fuzzy Taxonomy method

Since people around the world have gradually attached importance to resource conservation, various countries are actively taking measures to promote environmental protection and sustainable development. Green supply chain management (GSCM) have emerged in this context. Thus, in this essay, a novel intuitionistic fuzzy multiple attribute group decision making (MAGDM) method is designed to tackle this issue. First of all, CRITIC (Criteria Importance Through Inter-criteria Correlation) method is utilized to determine the weights of criteria. Later, the conventional Taxonomy method is extended to the intuitionistic fuzzy environment to compute the value of development attribute of each supplier. Then, the optimal one can be determined. Eventually, an application about green supplier selection in steel industry is presented, and a comparative analysis is made to demonstrate the superiority of the proposed method. The main features of the proposed algorithm are that they provide a practical solution for selecting GSCM and presents an objective weighting method to enhance the effectiveness of the algorithm.

TOPSIS Method for Probabilistic Linguistic MAGDM with Entropy Weight and Its Application to Supplier Selection of New Agricultural Machinery Products

In multiple attribute group decision making (MAGDM) problems, uncertain decision information is well-represented by linguistic term sets (LTSs). These LTSs are easily converted into probabilistic linguistic sets (PLTSs). In this paper, a TOPSIS method is proposed for probabilistic linguistic MAGDM in which the attribute weights are completely unknown, and the decision information is in the form of probabilistic linguistic numbers (PLNs). First, the definition of the scoring function is used to solve the probabilistic linguistic entropy, which is then employed to objectively derive the attribute weights. Second, the optimal alternatives are determined by calculating the shortest distance from the probabilistic linguistic positive ideal solution (PLPIS) and on the other side the farthest distance of the probabilistic linguistic negative ideal solution (PLNIS). This proposed method extends the applications range of the traditional entropy-weighted method. Moreover, it doesn’t need the decision-maker to give the attribute weights in advance. Finally, a numerical example for supplier selection of new agricultural machinery products is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easy to calculate. The proposed method can contribute to the selection of suitable alternative successfully in other selection problems.