Fuzzy Scale Research Articles

SmartISM 2.0: A Roadmap and System to Implement Fuzzy ISM and Fuzzy MICMAC

Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way.

The application of the Fuzzy Analytical Hierarchy Process (FAHP) as a multi-criteria analysis tool for road risk assessment: Algeria as a case study

In response to the escalating road safety challenges and the persistent increase in traffic accidents, it is imperative to adopt an integrated approach to address this multifaceted issue effectively. The main objective of this research is to classify or identify high-risk areas by studying the impact of the most significant factors contributing to road accidents. The methodology is based on a hierarchical model of three levels, including the essential factors that increase road risk. Subsequently, a fuzzy hierarchy process (FAHP) is conducted using a fuzzy scale to evaluate and analyse the most influential factors to determine the risk level for each road segment. This approach has been applied to the motorway in Algeria, specifically in the Mascara region, where the road was divided into 83 sections of 100 metres. The study's results revealed that factors related to infrastructure and those associated with drivers had the most significant impact, while environmental factors had less influence on road safety. The studied area exhibited risk levels ranging from moderate to low. The current state analysis of the roads (open to traffic) allows for the evaluation of factors that heighten road risks, this comprehensive evaluation is instrumental in formulating sustainable road safety strategies to mitigate future traffic accidents.

Methods of accounting for measurement uncertainty and semantic uncertainty in the classification of the state of objects according to quality indicators

The paper proposes methods of accounting for measurement uncertainty and semantic uncertainty when consistently using measurement and classification procedures to classify the state of objects according to their quality indicators. Methods of accounting for uncertainty are distinguished by two approaches: the first one is based on accounting for measurement uncertainty when constructing a classification scale, and the second one is based on presenting the result of measuring a quality indicator as a fuzzy number, the carrier of which is the expanded measurement uncertainty. Using these methods, the state of the object can be classified according to one or more categories of the classification scale, indicating the degree of belonging to these categories. Recommendations for the application of two approaches to account for measurement uncertainty in the subsequent classification are given. The use of one or more fuzzy classification scales is recommended when classifying the state of an object by a single quality indicator or by several quality indicators that are combined into a group indicator after classification using the fuzzy logic operators, such as arithmetic mean, as well as pairwise volume operators. When using single and group quality indicators, measurement uncertainty may be accounted for when constructing a classification scale, that is, when using a fuzzy scale. At the same time, the components of the measurement uncertainty preceding the classification and the vagueness of the expert information that may be used in the classification can be accounted for. In this way, the following components of the uncertainty can be defined: instrumental uncertainty and semantic uncertainty. The use of a clear classification scale when presenting the measurement result as a fuzzy number is recommended when classifying the state of the object according to a complex quality indicator that is related to individual quality indicators by a certain dependence or by accounting for the weighting factors of individual quality indicators. It is shown that when accounting for the measurement uncertainty, the state of the object may be classified into one or several categories of the classification scale, indicating the degree of belonging to each category.

Applying a Genetic Algorithm to Implement the Fuzzy-MACBETH Method in Decision-Making Processes

This paper describes the development of an evolutionary algorithm for building cardinal scales based on the Fuzzy-MACBETH method. This method uses a triangular fuzzy numbers scale in the MACBETH method to incorporate the subjectivity of a semantic scale into mathematical modeling, which enables circumventing the cardinal inconsistency problem of the classical method, facilitating its application in complex contexts. A genetic algorithm is used in the fuzzy system developed here to build the basic fuzzy scale in a cardinally inconsistent decision matrix. The proposed technique is inspired by crossover and mutation genetic operations to explore potential solutions and obtain a cardinal scale aligned with the decision maker’s preferences. Finally, an illustrative example of the application of the proposed decision support system is presented. The results confirm that the FGA-MACBETH method aligns with the classical method. This study’s primary contribution is that circumventing the problem of cardinal inconsistency in a semantically consistent decision matrix enabled obtaining a cardinal scale without requiring the decision maker to redo his/her initial assessments.

BHARAT: A simple and effective multi-criteria decision-making method that does not need fuzzy logic, Part-1: Multi-attribute decision-making applications in the industrial environment

A simple and effective multi-criteria decision-making methodology named as “Best Holistic Adaptable Ranking of Attributes Technique (BHARAT)” is proposed that can be used in single- as well as group decision-making scenarios of the industrial environment. The attributes data for various alternatives can be quantitative or qualitative (i.e., expressed in linguistic terms). This paper proposes to transform the qualitative attributes into quantitative attributes by means of simple linear scales rather than complex fuzzy scales. The proposed BHARAT method normalizes the data with reference to the “best” alternative corresponding to an attribute and the normalization procedure is repeated for all the attributes to get the normalized data. A group of decision-makers or a decision-maker assigns ranks to the attributes according to how important they are deemed to be, and these ranks are then transformed into the proper weights. The total scores of the alternatives are calculated by multiplying the weights of the attributes by the corresponding normalized data of the attributes for different alternatives. Four industrial case studies are presented to illustrate the potential of the suggested BHARAT method. The first case study deals with the problem of an automated warehouse selection for a large industrial plant involving a single decision-maker, 13 attributes, and 4 alternative warehouses; the second case study deals with the problem of sustainable maintenance service provider selection for a large petrochemical plant involving fuzzy group decision-making with 5 decision-makers, 9 attributes, and 4 alternative maintenance service providers; the third case study deals with the problem of alternative strategy selection for implementation of a make-to-order system for passenger car manufacturers involving 6 factors, 18 sub-factors, and 3 alternative strategies; and the fourth case study deals with the problem of process parameters selection in a sustainable high speed turning operation involving 4 attributes and 9 alternative sets of experimental conditions. The results of the proposed decision-making method and its second version are compared with the other popular decision-making methods. The proposed method and its another version are proved simple, effective, powerful, flexible, easy to apply, do not require the use of fuzzy logic, offer logical and consistent procedures to assign weights to the attributes, and are applicable to different decision-making scenarios of the industries. Part-1 of this paper describes the applications of the BHARAT method to multi-attribute decision-making problems and Part-2 describes the evaluation of Pareto solutions using the BHARAT method in multiple objective decision-making problems.

Urban green parks as sociomental landscapes: Understandings from Iran’s case study

This paper aims to investigate the impact of complexity and mystery on the perceived legibility of citizens in urban parks. The theoretical framework is based on Rachel and Stephen Kaplan’s theory (four cognitive states) and Lynch’s legibility theory. The method is descriptive correlational. Data collection was done through a survey questionnaire and mental maps. The paper uses Lynch’s mental maps and asks people to draw some aspects of the parks they remembered the most and compare the results with the surveys. The data analysis uses descriptive and inferential techniques as well as logical reasoning. The research claims a significant correlation with a higher-than-average coefficient between the legibility indicators (path, edge, node, district, landmark) and complexity and mystery in urban parks. The conclusion is that districts in urban parks have the most significant impact on the overlapping of citizens’ collective mental maps. Landmarks, paths, edges, and entrances are almost equally and moderately fuzzy-scale on the legibility of citizens’ mental maps in urban parks. Nodes play less of a role in citizens’ collective mental maps than other park elements. Parks with too many entrances, non-linear paths, indistinguishable districts, and undefined edges are not perceptible to citizens, and citizens’ perceptions of space differ significantly from the existing reality. The correlation between legibility dimensions, such as edges, nodes, landmarks, districts, paths, and entrances, is higher than the fuzzy scale average. Moreover, the legibility in each dimension will affect legibility dimensions a lot. Lynch’s theory of legibility in the spatial perception of parks and Kaplan’s theory of complexity and mystery criteria appear to be not fully responsive. It differs based on the features of each park, such as openness and closeness. For future research, it is better to comprehensively understand by using all dimensions of Kalpan’s theory: complexity, mystery, refuge, and prospect.

APPLICATION OF FUZZY ANALYTICAL NETWORK PROCESS IN DETERMINING THE CHOICE OF AREAS OF INTEREST

The Untan Statistics Study Program offers students a choice of areas of interest to develop competencies, attitudes, and skills. This study aims to analyze the decision to determine the choice of field of interest according to lecturers and students using the Fuzzy Analytical Network Process (FANP) method. A combination of ANP methods and Fuzzy logic, FANP is used to model and analyze complex networks of several factors determining the choice of areas of interest. The step in this study begins with the determination of the criteria and sub-criteria used for tissue formation. Then a comparison was carried out in pairs using the Fuzzy scale, so that the calculation of the global weight value of each criterion and sub-criteria was obtained. The resulting weight can be used for decision making. Data in research affects the opinions of lecturers and students. The decision obtained using the FANP method in this study is in the opinion of lecturers and students that the fields of business and finance are priority alternatives with the highest weight of 44.5%. The second priority with a weight of 37.5%, namely social and industrial interests, and the environmental and disaster sector occupies the last priority with a weight of 18%.

Fuzzy Indicators of the Forecast of Environmental Safety Taking into Account the Impact of Natural and Technosphere Factors

The emergence and uncontrolled development of environmental hazards in the processes of life in the absence of appropriate response measures in many cases leads to the emergence of man-made emergencies with dangerous consequences for public health and the environment. It is proposed to evaluate the results of complex monitoring and forecasting of these dangerous processes in a new format of creating a fuzzy scale of indicators of the level of environmental safety of life support of territories based on the theory of fuzzy sets. The study aims to develop a fuzzy scale of indicators for predicting the state of environmental protection. Protection of the population and territories from environmental threats and possible man-made emergencies. Within the framework of the purpose of the study, a fuzzy scale of threshold levels of environmental safety of territories and economic objects has been developed based on the theory of fuzzy sets and the mathematical apparatus of soft computing. The practical significance of the developments is confirmed by the successful application of the proposed scale of indicators and indicators in assessing the environmental hazard of life support systems of settlements and industrial enterprises affecting the environment.

On the Band-Limited Information Throughput of Free-Selective and Free-Responsive Spatially Non-Local Perception

A single-blind experiment was conducted on free-responsive spatially non-local perception of free-selective simple photographic targets. One author (the tasker) chose a photographic target not subjected to a priori compiling, and the other author (the perceiver) attempted to unconventionally perceive the target. Feedback was expected prior to a new target being selected. A hundred trials were completed over 11 months. Thirteen judges offered gradings that collectively projected an apparent information requisition yield (AIRY). The AIRY refers to two aspects of the matching of a taskette (a target’s counterpart) with respect to the target based on fuzzy scales from 0% to 100%, graded with an increment of 10%. The type-I or “tangibility” aspect of matching assessed the extent of the resemblance of a taskette to the target in terms of the shape or structural details of the primary physical entity/event. The type-II or “intangibility” aspect of the matching assessed the degree of the reconstruction of a taskette to the target in terms of the feature or functionality of the predominant physical entity/event. A total of 6 taskettes received >50% grades in both type-I and type-II aspects of matching with their respective targets. This outcome may correspond to an effect size of 1.47 if the chance-expectation may give a benchmark proportion of 3 out of 100 trials producing >50% grades in both type-I and type-II aspects of matching. Patterns indicating target interference associated with two trials entail further investigation. The approaches offer new insights to quantifying the band-limited information throughputs of spatially non-local perception.

Development of plant-based fish analog: Optimization using D-optimal mixture design and fuzzy analysis, Characterization and shelf life study

This study aims to develop an analog fish product with comparable nutrients using plant-based materials such as oyster mushrooms, nori, and kombu and grade them on the 9-point hedonic scale for sensory characteristics such as appearance, aroma, juiciness, texture, and taste. Oyster mushrooms are high in protein and have an umami flavor amplified by adding seaweeds such as nori and kombu to give them a fishy flavor. The mushroom has a flaky texture that helps it resemble the fish's texture. We combined hydrated mushrooms and seaweeds with maize flour, baking powder, salt, and MSG to make the balls. The concentration of ingredients such as oyster mushroom, nori, kombu, baking powder, salt, and MSG was optimized using one-factor optimization. The final concentration of 6 % nori, 4 % kombu,24 % corn flour, 0.9 % baking powder, 1 % salt, 1 % MSG, and 63.1 % oyster mushroom using sensory data. Response surface methodology was also used to predict the appropriate model for the production of the fish analogs in which 8 % nori, 6 % kombu,23.981 % corn flour, 0.6 % baking powder, 0.81 % salt, 1.2 % MSG, and 59.4 % oyster mushroom using the D- optimal mixture design method. The sensory characteristics of vegan fish analogs were analyzed using the fuzzy scale (F1–F6), and samples were screened based on similarity values. The shelf life study was also carried out using an optimized formulation by treating with citric acid, adding preservatives like sodium benzoate and potassium sorbate, and in combination with potassium sorbate and citric acid. During the storage, antioxidant capacity, phytochemical analysis, rancidity study, moisture content, and microbial study were recorded.

Decision Support System for Evaluation and Ranking of Robots Using Hybrid Approach

A hybrid approach namely “fuzzy modified distance based approach (FMDBA)” is proposed and presented for the selection and ranking of industrial robots, which is hitherto not applied in open literature for the purpose. The priority weights of the selection criteria/sub-criteria and performance ratings of the alternative robots for subjective selection criteria/sub-criteria are obtained on ten point and seven-point fuzzy scales using experts’ opinion. Statistical methods are used for ensuring reliability of the data obtained from experts. FMDBA method can incorporate interaction among selection criteria/sub-criteria into the decision analysis, an issue that has been generally overlooked in earlier robot selection studies. The robots are ranked according to the Euclidian distance of alternative robots termed as composite distance from the hypothetically imagined best possible robot. The methodology is illustrated through an example using a previously published data set of 27 robots based on four selection criteria. The results are compared with those of other multicriteria decision aids, which have been also employed in earlier studies addressing robot selection. A decision support system, “ROBORANK,” is developed, which is user friendly and also does not require an extensive technical knowledge of the selection criteria/sub-criteria and or robots for its use.

Selection of promotional media on tourist boats with fuzzy AHP and fuzzy TOPSIS

The tourism sector necessitates effective promotion to introduce tourist attractions, including tourist boats operating on the Mahakam River. Given the prevalence of the digital era, it is imperative to employ appropriate promotional channels that precisely target the intended audience. The selection of promotional media entails various criteria and alternatives, thereby making it highly suitable to employ a multi-criteria decision-making approach. Due to the inherent uncertainty in assessments, a fuzzy scale becomes indispensable. This research utilizes a combination of Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to address this issue. The findings of this study reveal that the order of importance for the criteria is as follows: Audience fit (C1), Reach (C2), Interactivity (C3), Cost (C4), Look and Feel (C5), and Frequency (C6). Regarding the selection of promotional media, social media (A1) emerges as the most effective medium when compared to web-based platforms (A2) and mobile applications (A3). The amalgamation of these two methodologies (Fuzzy AHP and Fuzzy TOPSIS) for the purpose of selecting tourism promotion media in Indonesia remains largely unexplored in prior research. Therefore, this study exhibits novelty and contributes to the existing knowledge concerning Multicriteria Decision Making and the selection of tourism promotion media in Indonesia.

Fuzzy-MACBETH Hybrid Method: Mathematical Treatment of a Qualitative Scale Using the Fuzzy Theory

This paper describes the research procedures adopted in developing a triangular fuzzy number scale based on the semantic scale of MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique). The objective was to mathematically treat the uncertainty and subjectivity of linguistic variables used to assess a decision problem. A matrix was initially obtained based on a decision maker’s assessment of a given context analysis. This decision matrix was then fuzzified based on a triangular Fuzzy numbers scale. Next, the inference process was performed using F-LP-MACBETH linear programming problem proposed here, resulting in a Fuzzy scale. This scale was then defuzzified using the centroid method, from which a crisp basic scale emerged, which was then cardinalized. The results show that the MACBETH Fuzzy method proposed here can overcome the classical method’s cardinal inconsistency problem, which facilitates its application in complex contexts. Hence, the MACBETH Fuzzy Hybrid method generated numerical values based on the decision makers’ semantically consistent assessments in a decision matrix, which by the classical method presents cardinal inconsistency. Therefore, the advantage of the proposed method consists in the possibility of obtaining a cardinal scale aligned to the decision makers’ preferences without the need to reassess the context.

Selection of sustainable and green suppliers using a fuzzy R-method in group decision-making situations

A growing number of companies treat "sustainability" as an important objective in their strategy. Sustainable and green suppliers increase efficiency by reducing costs and waste in industries. Sustainable and green supplier selection is a critically important factor in moving forward along the path of sustainability. A fuzzy R-method is proposed in this paper based on ranking the fuzzy numbers of alternative suppliers and criteria. Triangular fuzzy membership function and different fuzzy scales are presented to demonstrate and validate the proposed method. Fuzzy composite scores are generated, and these scores are converted into crisp forms to evaluate the alternative suppliers. The novelty of the proposed method is that it is simple, can deal with qualitative and quantitative criteria of supplier selection, and requires less computational effort for evaluating and ranking the green and sustainable suppliers in fuzzy group decision-making situations. The weights of the criteria and alternative suppliers are generated using a simple equation and hence there is no need to apply different methods for weights generation and ranking. Furthermore, this method does not require normalization of the data. Two realistic group decision-making problems of green and sustainable supplier selection to test the method. Sensitivity analysis for the proposed method is also conducted to check the consistency of the proposed method to different weights of the criteria. The proposed method is effective, robust, and competitive to the existing multi-criteria decision making (MCDM) methods of sustainable supplier selection.

Assessment of soil fertility for wheat using combined Fuzzy and AHP techniques in Udham Singh Nagar of Uttarakhand, India

Assessing soil fertility is crucial for developing effective soil management strategies that can enhance soil health, increase crop productivity, and promote sustainable agricultural practices. The current study was conducted to assess the soil fertility index and, to prepare a soil fertility zonation map using combine fuzzy and analytical hierarchy process (AHP) approaches in Udham Singh Nagar of Uttarakhand. Sixty GPSbased surface soil samples were collected (0-30 cm depth) from different locations, and analyzed chemical properties using a stratified multistage random sampling method and maps were prepared to identify their spatial distribution. The results show that the values of soil fertility index on the fuzzy scale (0-1) was varied from 0.04-0.62 and, therefore, the study area was classified as very low, low, and moderate soil fertility classes comprising 55.61%, 44.24% and 0.14%, respectively. AHP analysis revealed that the most important limiting factor for wheat production was available nitrogen, followed by phosphorous, potassium, organic carbon, pH and electrical conductivity. A correlation coefficient between wheat yield and soil fertility index was found to be as high as 0.86, and its validating the zonation of soil fertility classes. This study infers that combined fuzzy-AHP techniques may be used to compute soil fertility index and limiting factors of wheat production.

The decision-making process in learners’ failure using fuzzy scale

This study aims to determine how to use an ambiguous scale to assess student performance and proficiency, whether students perform well or poorly in subjects such as reading comprehension, vocabulary, and punctuation. In this study, Sugeno Integral is a good choice for decision-making. Although other scales may be better than the one used, Sugeno Integral is easier to use than different scales, as evidenced by the results obtained.

Fuzzy Scale Reliability Assessment for Analysis of Mathematics Learning Styles

Fuzzy Scale Reliability Assessment for Analysis of Mathematics Learning Styles

Evaluation and selection of large cardamom supply chain using fuzzy logic based decision‐making model

AbstractLarge cardamom is a high‐value, low‐volume cash crop that is commercially important. The evaluation and selection of suppliers is a complicated and common multi‐criteria decision‐making challenge. Supplier selection becomes more difficult and unpredictable as a result of the necessity for human judgment in many areas of supplier selection, such as preferences for alternatives or supplier qualities. The objective of the study was to evaluate various large cardamom supply chains based on various supply chain quality parameters. The proposed selection strategy used fuzzy logic to identify the best acceptable supplier as well as rank the various supply chain parameters. The major steps of supply chain evaluation by fuzzy modeling involve the calculation of triplets for the score of various supply chains, calculation of triplets for the score of supply chain quality attributes, and calculation of overall membership function on a standard fuzzy scale. The results revealed that the proposed fuzzy logic method was well suited as a decision‐making tool for supplier evaluation. The assessment of various supply chains showed that the best supply chain management method included material moving from producers to consumers through aggregators, commission agents, wholesalers, and retailers. Comparing the overall similarity values of each chosen supply chain, customer satisfaction was ranked top among the quality attributes of the supply chain, followed by external and internal support, internal performance, and strategic and operational coordination. Consumers monitoring the performance of the supply chain can assist in information acquisition, influence the behavior of supply chain participants, and enhance overall performance.Practical applicationSelecting a suitable large cardamom supply chain would provide a solution for regulating the flow of products and information across the supply chain network. Implementing fuzzy logic for preferred supplier selection has a significant impact on the financial perspective. In the fuzzy logic for a large cardamom supply chain, fuzzy sets or membership functions were employed to express input space to output space. Fuzzification was used to transform a vector of crisp input data acquired from a study of a large cardamom supply chain into a vector of equivalent input language variables. The fuzzy output variables are defuzzified in the defuzzification process, and the fuzzy variables are turned into crisp values or crisp output vectors.

A Fuzzy Ecosystem Benchmarking for Crowdfunding in Transport Sector

The global crowdfunding (CF) market was valued at 10.2 billion US$ in 2018 and is expected to almost triple in size by 2025. The CF is evolving as a major and easy source of fundraising methods for various industries. Still, this acceptability is not widely accepted in transportation activities due to various limitations and low awareness among policymakers. The present research analyzes the factors contributing to the growth of market acceptability of CF, divided into three different research phases: identifying barriers from the literature, interviews with transport industry experts at two stages, and designing an ISM model in a fuzzy environment. The identification phase led to selecting 16 factors from the past literature and suggesting industrial experts. The Interpretive Structural Modelling (ISM) analysis was used to understand the impact and linkage of identified barriers on seven levels of the fuzzy scale. The factors are classified into four major categories based on the fuzzy matrix's drive and dependence power using Fuzzy MICMAC. The sixteen identified growth factors for CF have been distributed in 5 levels in the ISM designed model. All the factors had fallen in only two quadrants of MICMAC based on the fuzzy scale matrix. Except for No or Low in regulation, the selected fifteen factors fall in the linkage quadrant, with high dependency and driving power. Such relation of all variables is the precise reason for storm growth in the field. “No or Low in regulation” is one of the most significant factors to the growth and acceptance of this innovative fundraising method by common investors but cannot be controlled directly by the associated crowdfunding members in the transport industry.

Incorporating management opinion in green supplier selection model using quality function deployment and interactive fuzzy programming.

The need for environmental protection and involvement of ecological aspects in the business operations is forcing the organizations to re-examine their action plans and rebuild their supply chain activities. Many organizations are incorporating environmental rules and regulations in their everyday matters by focusing on green supplier selection. The proposed research paper develops a multi-objective interactive fuzzy programming model for the selection of suppliers. This model works on a business quartet of green appraisal score, cost, quality, and time. The model uses an environmental scale for different green parameters and all the suppliers are scored based on this scale. In this research model, Quality Function Deployment (QFD) methodology is integrated with the multi-objective interactive fuzzy programming. QFD technique is utilized to compute the weights of several green factors used for the selection of suppliers. The model uses a Fuzzy linguistic scale and a triangular membership function to link expert opinions along with their experience to solve the problem. Finally, the model is validated on a numerical case study of the textile industry for green supplier selection which achieves a 100% satisfaction for cost and time, 75% satisfaction for green appraisal score, and 93.95% for the quality. The proposed model assists the decision-makers in selecting green suppliers to improve the overall sustainability of their organizations.