2-tuple Fuzzy Linguistic Model Research Articles

A novel improved FMEA method using data envelopment analysis method and 2-tuple fuzzy linguistic model

A novel improved FMEA method using data envelopment analysis method and 2-tuple fuzzy linguistic model

A 2-tuple fuzzy linguistic model for recommending health care services grounded on aspect-based sentiment analysis

Evaluating the quality of health care systems usually entails the examination of objective variables (waiting time, patients per doctor, etc.). Nonetheless, other subjective variables can be used thanks to new Internet tools. Many online medical services let users convey their opinions on the offered services. That information is an interesting tool to measure the quality of these services. It describes sentiments about different features using a wide range of adjectives, adverbs and nouns, many times, completely different for each feature. Therefore, it is interesting to assess every feature individually using different scales. This study presents an application of a multi-granular fuzzy linguistic model to represent the opinions about the different features of health care systems with the aim of recommending hospitals according to the user preferences. To test this approach, the opinions from real hospitals have been assessed using different user preferences. The obtained results outperform other state-of-the-art approaches.

Group Decision-Making Model Based on 2-Tuple Fuzzy Linguistic Model and AHP Applied to Measuring Digital Maturity Level of Organizations

In the digital era, companies have experienced a progressive change in their business models, which has led them to develop greater agility to adapt to changing environments, and the necessity to properly manage the group decision making in these companies is patent. This paper shows how fuzzy models are utilized in group decision making. In this context, one of the most important group decisions to be adopted is how to manage the digital transformation of the company, drawing up the best possible roadmap. To achieve this goal, this paper proposes a new methodology improvement of group decision making based on a fuzzy 2-tuple linguistic model and the analytic hierarchy process (AHP) method. The suggested methodology proposes the calculation of the digital maturity level (DML) of companies based on five of the most cited and agreed upon criteria in the existing literature. The methodology proposed in this paper was tested and validated for the business case of Spanish SMEs through three different clusters to derive global recommendations tailored to each specific cluster and company segments, using a sample of 1428 companies. The main fundings reveal that the digital maturity level directly impacts on the size of the company and its digital mindset in the sense of driving change management processes. As future works, authors recommend extending the model to any industry using the proposed methodology and evaluate disruptive technologies such as artificial intelligence (AI) in supporting the digital transformation of SMEs.

Addressing Environmental Protection Supplier Selection Issues in a Fuzzy Information Environment Using a Novel Soft Fuzzy AHP–TOPSIS Method

With the current heightened promotion of environmental awareness, issues related to environmental protection have become a critical component of economic development. The emergence of new environment-friendly materials and simple packaging, and other environmental awareness demands in recent years, have prompted manufacturers to pay more attention to planning greener production and supply processes than before. Many scholars have been urged to investigate the issues related to environmental protection and the sustainable economy of green suppliers. However, many factors needed to be considered, such as the price, cost, benefit, reputation, and quality involved in the process of green supplier selection. These factors require quantitative and qualitative analysis information, making the issue of environmental protection a multi-criteria decision making (MDCM) problem. Traditional research methods are unable to effectively and objectively handle the MCDM problem of green supplier selection due to the problem’s complexity and the method’s inclination towards biased conclusions. To resolve the complicated problem of green supplier selection, this study combined the fuzzy analytic hierarchy process (AHP), the technique for order preference by similarity to ideal solution (TOPSIS), and the 2-tuple fuzzy linguistic model (2-tuple FLM) and corrected the ranking of the possible green suppliers. The computation results were also compared with the typical TOPSIS and AHP–TOPSIS methods. Through the numerical verification of the actual case for the green supplier, the test results suggested that the proposed method could perform an objective evaluation of expert-provided information while also retaining all their valuable insights.

Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain

Time series forecasting of passenger demand is crucial for optimal planning of limited resources. For smart cities, passenger transport in urban areas is an increasingly important problem, because the construction of infrastructure is not the solution and the use of public transport should be encouraged. One of the most sophisticated techniques for time series forecasting is Long Short Term Memory (LSTM) neural networks. These deep learning models are very powerful for time series forecasting but are not interpretable by humans (black-box models). Our goal was to develop a predictive and linguistically interpretable model, useful for decision making using large volumes of data from different sources. Our case study was one of the most demanded bus lines of Madrid. We obtained an interpretable model from the LSTM neural network using a surrogate model and the 2-tuple fuzzy linguistic model, which improves the linguistic interpretability of the generated Explainable Artificial Intelligent (XAI) model without losing precision.

A Novel Group Decision-Making Approach for Hesitant Fuzzy Linguistic Term Sets and Its Application to VIKOR

This paper develops a novel group decision-making (GDM) approach for solving multiple-criteria group decision-making (MCGDM) problems with uncertainty. The hesitant fuzzy linguistic term sets (HFLTSs) are applied to elicit the decision makers’ linguistic preferences due to their distinguished efficiency and flexibility in representing uncertainty. However, the existing context-free grammar for linguistic description cannot allow generating the linguistic expressions completely free to limit the richness of HFLTSs, and the related methods for dealing with HFLTSs also have limitations in aggregating HFLTSs with different lengths and types. Therefore, this paper proposes extended context-free grammar and a novel GDM approach for HFLTSs, considering the advantages of the rough set theory and OWA operators. The rough set theory can manage the uncertainty existing in the fuzzy representation and deal with HFLTSs represented by the 2-tuple fuzzy linguistic model to get rough number sets. The OWA operator can aggregate these sets with different numbers of elements into an interval simply and objectively. Then, an extended VIKOR method based on the proposed GDM approach for HFLTSs is presented to solve the MCGDM problems. Finally, two examples are given to illustrate the applicability and validity of the developed GDM approach and the hesitant VIKOR method through sensitivity and comparison analysis with other existing approaches.

Aplicación de un enfoque multigranular basado en el modelo lingüístico difuso 2-tupla para la evaluación de indicadores de política foresta

Introduction: The need for quality indicators is well recognized by users and proponents of public policy evaluation. Indicators recurrently include qualitative attributes for which there are few studies assessing the level of compliance. Objective: To apply a multigranular approach, based on the 2-tuple fuzzy linguistic model, to evaluate 13 indicators of the National Forestry Program, established in the system of social policy indicators derived from the National Development Plan 2012-2018 of Mexico. Materials and methods: The method uses the 2-tuple fuzzy linguistic representation model and an extension called extended linguistic hierarchies, designed to solve problems with multigranular linguistic information. The indicators’ level of compliance was evaluated based on four criteria: clarity, relevance, monitoring, and adequacy. Results and discussion: The structure defined in evaluating social policy indicators corresponds appropriately to that used with the 2-tuple fuzzy linguistic model. The evaluation resulted in a sorted list in which the indicator “Rate of change of timber forest production” had the best rating with a “very high” level of compliance; 10 other indicators had the “high” level of compliance, and the remaining two indicators were rated with “moderate” compliance. Conclusions: The 2-tuple fuzzy linguistic model allowed the appropriate evaluation of the level of compliance with the desirable attributes of indicators

A 2-tuple Fuzzy Linguistic Model for Criteria Evaluation on Technological Innovation of Marine Enterprises

Zhang, Q., Liu, S., and Tu, Q., 2019. A 2-tuple fuzzy linguistic representation model for evaluation index selection of technological innovation of marine technology enterprises. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 634–639. Coconut Creek (Florida), ISSN 0749-0208.With the depletion of terrestrial resources, the ocean has become an important resource for the development of countries around the world. As the basic means and supporting force for marine development, marine technology innovation has thus become the main field of national competition. Hence, as the inner motivation and main source of marine technology enterprise development, technological innovation plays an important role in competition. Regarding of the multiple criteria decision-making problem of technological innovation on marine technology enterprises, this paper proposes a method for evaluation index selection by combining 2-tuple fuzzy linguistic approach. Experts evaluate each indicators of the candidate index set and provide the degree of understanding to the index when evaluating the information, which can effectively distinguish the quality of information provided by various experts. Finally, the paper integrates the evaluation information by multiple experts using the aggregated operators based on 2-tuple linguistic model and formed the scientific index system for technological innovation of marine technology enterprises.

Adaptive Learning Model for Predicting Negotiation Behaviors through Hybrid K-means Clustering, Linear Vector Quantization and 2-Tuple Fuzzy Linguistic Model

Acknowledged System of systems (SoS) manager has no direct control over contributing systems yet they deliver capabilities required to meet the purpose of the SoS operating in an interdependent environment. Forming a joint capability by contribution requires both persuasion and negotiation between the SoS coordinator and each individual system. Negotiation here involves multiple parties, and multiple issues. The challenges here include predicting an opponent system's negotiation behavior and reaching a near optimal negotiation outcome based on the following three issues: performance demands made by the coordinator, monetary benefits in lieu of effort required, and the deadline assigned to prepare for the final SoS participation formation. The negotiation framework proposed involves an unsupervised clustering of the difference between offers from both parties on all three issues by clustering techniques. The clustering results so far indicate the presence of four prominent behaviors: selfish, semi-cooperative, opportunistic, and extremely selfish. The clustered data was used to train both a radial bass function network (RBFN) and a linear vector quantization network (LVQN) to predict future offers. Once trained, the SoS manager uses 2-tuple fuzzy linguistic representation model for decision making. A time-dependent strategy is used to generate a new offer by SoS manager to the systems.

Selecting the Individual Numerical Scale and Prioritization Method in the Analytic Hierarchy Process: A 2-Tuple Fuzzy Linguistic Approach

The validity of the priority vector used in the analytic hierarchy process (AHP) relies on two factors: the selection of a numerical scale and the selection of a prioritization method. The traditional AHP selects only one numerical scale (e.g., the Saaty scale) and one prioritization method (e.g., the eigenvector method) for each particular problem. For this traditional selection approach, there is disagreement on which numerical scale and prioritization method is better in deriving a priority vector. In fact, the best numerical scale and the best prioritization method both rely on the content of the pairwise comparison data provided by the AHP decision makers. By defining a set of concepts regarding the scale function and the linguistic pairwise comparison matrices (LPCMs) of the priority vector and by using LPCMs to unify the format of the input and output of AHP, this paper extends the AHP prioritization process under the 2-tuple fuzzy linguistic model. Based on the extended AHP prioritization process, we present two performance measure criteria to evaluate the effect of the numerical scales and prioritization methods. We also use the performance measure criteria to develop a 2-tuple fuzzy linguistic multicriteria approach to select the best numerical scales and the best prioritization methods for different LPCMs. In this paper, we call this type of selection the individual selection of the numerical scale and prioritization method. We also compare this individual selection with traditional selection by using both random and real data and show better results with individual selection.