2-tuple Linguistic Representation Research Articles

An Extension of the ELECTRE III Method based on the 2-tuple Linguistic Representation Model for Dealing with Heterogeneous Information

An Extension of the ELECTRE III Method based on the 2-tuple Linguistic Representation Model for Dealing with Heterogeneous Information

Sustainable mining method selection by a multi-stakeholder collaborative multi-attribute group decision-making method

How to protect the ecological environment during mineral resource exploitation is a crucial issue for the survival and development of human society. In this context, mining method selection requires simultaneous achieving environmental protection and resource exploitation, while involving the coordination of multi-agent interests. Therefore, this study develops a multi-stakeholder collaborative multi-attribute group decision-making approach for mining method selection. This method enables multiple stakeholders and experts from different fields to make decisions together. First, a conflict resolution model is proposed to resolve cognitive conflict and interest conflict among multi-stakeholders. Then, the 2-tuple linguistic representation model is used to deal with the decision maker's fuzzy preference. Next, the comprehensive attribute weights are calculated and the alternatives are prioritized. A case study for mining method selection in a gold mine in Shandong Province, China, is presented to verify the validity of the proposed method. This method considers not only the coordination between mineral resource exploitation and ecological environment protection but also the conflicting views of stakeholders. The proposed method is a useful tool for the assessment and selection of development schemes involving multiple stakeholders.

Configuration optimization of product-service system design requirements based on hesitant information axiom

Product-service system (PSS) has attracted attention of manufacturers to shift from product-providing to solution-providing, which is a marketable set of products and services. The existing researches emphasize the fulfillment of individualized customer requirements through different PSS configurations. The PSS planning phase is of high importance in generating conceptual schemes, which translates customer requirements (CRs) to design requirements (DRs). In this paper, a systematic decision-making approach based on QFD is put forward aiming to configure the PSS design requirements (DRs). To address the uncertainty and hesitancy in QFD modeling, a hesitant fuzzy linguistic term sets (HFLTSs) is applied to elicit the experts’ linguistic preferences in evaluating the importance of CRs and the relationships between CRs and DRs. To dealing with the group decision-making problems concerning the HFLTSs, the min-upper operator and the max-lower operator assemble the experts’ evaluation results into a linguistic interval, and then the numerical results can be obtained by using the 2-tuple linguistic representation model and the interval preference degree computation. A non-linear 0-1 programming model is proposed to select the target DRs’ specifications for maximizing customer satisfaction under cost constraint. In order to objectively determine the satisfaction degree of each optional specification of DR, the information axiom is introduced to construct the objective function via information content computation. To deal with the qualitative DRs, HFLTSs and information axiom are combined and hesitant information axiom (HIA) is proposed. Finally, a DRs optimization model is established using HIA and the imprecision method. A case study is carried out to demonstrate the effectiveness of the optimal PSS planning approach developed.

Integrating Subjective–Objective Weights Consideration and a Combined Compromise Solution Method for Handling Supplier Selection Issues

The development of artificial intelligence and the recent COVID-19 pandemic as well as the emergence of other diseases has led to dramatic changes in the overall supply chain development. The choice of a suitable supplier will be the key to ensuring sustainable development of the company and the normal operation of the overall supply chain. However, the evaluation data of the supplier selection contains both qualitative and quantitative data simultaneously. In addition, the evaluation information provided by experts often includes some incomplete and hesitant information. These reasons lead to the complexity of supplier selection. Traditional supplier selection calculation methods ignore the objective weight considerations and thus lead to biased assessment results. The main goal of this study is to overcome the limitations of conventional supplier selection methods, fully consider the subjective and objective weights of the evaluation criteria and deal with incomplete information for providing more correct supplier ranking results. A stepwise weight assessment ratio analysis (SWARA) method, the 2-tuple linguistic representation method, and the combined compromise solution (CoCoSo) were applied in this study to solve the problem of supplier selection. To verify the rationality and correctness of the proposed method, the third-party logistics supplier selection was used as the illustrated example in the numerical validation section. The simulation results confirm that the proposed method can effectively deal with supplier selection with unclear information and can provide more reasonable supplier ranking results.

Extended MABAC method based on 2-tuple linguistic $ T $-spherical fuzzy sets and Heronian mean operators: An application to alternative fuel selection

<abstract><p>In recent years, fossil fuel resources have become increasingly rare and caused a variety of problems, with a global impact on economy, society and environment. To tackle this challenge, we must promote the development and diffusion of alternative fuel technologies. The use of cleaner fuels can reduce not only economic cost but also the emission of gaseous pollutants that deplete the ozone layer and accelerate global warming. To select an optimal alternative fuel, different fuzzy decision analysis methodologies can be utilized. In comparison to other extensions of fuzzy sets, the $ T $-spherical fuzzy set is an emerging tool to cope with uncertainty by quantifying acceptance, abstention and rejection jointly. It provides a general framework to unify various fuzzy models including fuzzy sets, picture fuzzy sets, spherical fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets and generalized orthopair fuzzy sets. Meanwhile, decision makers prefer to employ linguistic terms when expressing qualitative evaluation in real-life applications. In view of these facts, we develop an extended multi-attributive border approximation area comparison (MABAC) method for solving multiple attribute group decision-making problems in this study. Firstly, the combination of $ T $-spherical fuzzy sets with 2-tuple linguistic representation is presented, which provides a general framework for expressing and computing qualitative evaluation. Secondly, we put forward four kinds of 2-tuple linguistic $ T $-spherical fuzzy aggregation operators by considering the Heronian mean operator. We investigate some fundamental properties of the proposed 2-tuple linguistic $ T $-spherical fuzzy aggregation operators. Lastly, an extended MABAC method based on the 2-tuple linguistic $ T $-spherical fuzzy generalized weighted Heronian mean and the 2-tuple linguistic $ T $-spherical fuzzy weighted geometric Heronian mean operators is developed. For illustration, a case study on fuel technology selection with 2-tuple linguistic $ T $-spherical fuzzy information is also conducted. Moreover, we show the validity and feasibility of our approach by comparing it with several existing approaches.</p></abstract>

Consensus reaching model for counter-intuitive in D–S evidence theory and application under 2-tuple linguistic representation

In the information fusion field, Dempster–Shafer (D–S) evidence theory is a multi-source technology to solve the uncertain problems. With the aim of improving the decision accuracy, D–S evidence theory can make full use of information from different sources which are redundant and complementary. However, the influence caused by conflicting evidence during information fusion, named the counter-intuitive result, will confuse the selection of decision-makers (DMs). Inspired by the distance-based uncertainty measure, which is a typical technique used to manage uncertain information, the consensus reaching model is applied to overcome the influence caused by conflicting evidence in this paper. Considering the hesitant and uncertain of cognition, 2-tuple linguistic representation method is introduced to model and manage this vague decision information given by DMs. Finally, a consensus reaching model for counter-intuitive result in D–S evidence theory is put forward. To verify the effectiveness of the proposed method, the selection of plant protection machine suppliers is modeled as a multi-criteria decision-making (MCDM) problem. According to the decision-making process, the best option for plant protection machine suppliers is obtained. The decision result indicates a strong correlation between the consistency value and conflicting value of evidence.

Understanding consumer preference through fuzzy-based recommendation system

We introduce a new recommendation technique based on fuzzy concepts such as the 2-tuple fuzzy linguistic representation and the fuzzy market research system approach. The recommendation technique uses general consumer perception about product attributes of multi-attributed products such as smartphones as input and gives consumers’ sequential preference of the products as output. The recommendation technique explains the power of an attribute in predicting other attributes, especially in fuzzy and uncertain environments, through the conceptualisation of consumers’ innate associations between the attributes. Our technique provides an understanding of consumer preference through the articulation of buyers’ trade-offs amongst attributes of a product.

Integration of SERVQUAL, Analytical Kano, and QFD using fuzzy approaches to support improvement decisions in an entrepreneurial education service

Quality tools such as Kano’s model, SERVQUAL, and Quality Function Deployment (QFD) have been used to design and improve services. Although these tools can be used separately, their integration can provide a deep analysis of the service quality, generating opportunities for its improvement. However, the effective integration of these tools requires the proper handling of their variables, which contains imprecision and uncertainties since they are based on customer’s perceptions. Fuzzy approaches are feasible alternatives to overcome these limitations and integrate these tools. Nevertheless, sophisticated fuzzy methods can deal better with these limitations. This article proposes an integrative framework involving SERVQUAL, Analytical Kano (A-Kano), and QFD using fuzzy approaches (Fuzzy Inference System and 2-tuple fuzzy linguistic representation). The framework comprises four main phases concerning (i) the identification of quality attributes and service processes using the A-Kano and SERVQUAL; (ii) the integration of these two quality tools using the Fuzzy Inference System (FIS); (iii) the integration between A-Kano and SERVQUAL output and QFD matrix using 2-tuple fuzzy linguistic representation; and (iv) the identification of improvement projects to address the opportunities identified in the previous phases. The proposed integrative framework was tested and validated in an entrepreneurial education company that provides experiential services. It contributes to providing a new method to assess the service quality perceptions, categorizing these perceptions according to their effects on customer satisfaction, prioritizing improvements, and identifying technical requirements. Mainly, this study presents a new proposal for integrating the SERVQUAL, A-Kano, and QFD using advanced fuzzy techniques, which contributes to overcoming the limitations found in the extant literature.

An approach for solving fuzzy multi-criteria decision problem under linguistic information

Linguistic information processing exists in multi-criteria decision making, and linguistic truth-valued lattice implication algebra (LTV-LIA) has definite advantages in handling comparable and incomparable linguistic values. To deal with the preference relations with linguistic evaluation information, we establish a novel approach for solving fuzzy multi-criteria decision problem under linguistic information based on LTV-LIA. In this paper, we propose linguistic lattice-valued preference relation (LLVPR). LLVPR positive and negative matrixes are introduced to evaluate the advantages and disadvantages of alternatives respectively. In order to get a reasonable result, we introduce a new algorithm to check and repair the consistency of a LLVPR. A linguistic lattice-valued 2-tuple representation model (LLV2-tuple) and some new aggregation operations are presented to get the comprehensive linguistic information without information loss. Considering different decision makers have different preferences, a multiple preferences implication operation of LLV2-tuple is introduced. Finally, we propose a novel linguistic analytic hierarchy process embedded in aggregation layer and implication layer, introducing algorithm and numerical examples. A comparative analysis is adopted to illustrate the rationality.

Multi-Criteria Decision-Making Method Based on a Weighted 2-Tuple Fuzzy Linguistic Representation Model

Multi-criteria decision-making (MCDM) methods are used in the selection and evaluation of alternatives. However, too many decision criteria and numerical calculations will increase the computational complexity and make the calculation process difficult to understand. In this paper, a weighted 2-tuple fuzzy linguistic representation model is proposed. The contributions of this study are as follows: (1) Feature selection method was used to remove the redundant or irrelevant feature attributes, thereby simplifying calculations and reducing calculation complexities. (2) The integration of the 2-tuple linguistic representation model simplifies the complexity of numerical calculations. The calculation of qualitative scales can be closer to the human thinking model, and loss of information can be avoided during calculations through the appropriate model. (3) Information fusion technology, i.e., ordered weighted average operator (OWA), was used. The method simplifies the traditional OWA calculation and can be calculated according to the priority order of the indicators. (4) Four major shareholding companies in Taiwan 50 ETF stocks were selected as experimental cases. In total, 992 tuples were obtained and 29 technical indicators were analyzed. The results indicate that case A1 is the most stable among the four stocks considered under different decision-making situations, and it has the first priority ranking.

Evaluation of Urban Industry-Education Integration Based on Improved Fuzzy Linguistic Approach

Integration of industry and education is a strategic measure to address the structural imbalance issues in the scientific and technological development and industrial transformation, as well as educational system. Currently, joint efforts of governments, universities, and enterprises have achieved improvements in integrating education and various related industries. However, the evaluation of industry-education integration has not received sufficient attention it deserves. Based on the context-input-process-product (CIPP) evaluation model, this paper proposes an evaluation index system for urban industry-education integration. Considering the vagueness and uncertainty of the indexes, the evaluation model is established using intuitionistic fuzzy analytic hierarchy process (IFAHP) method and 2-tuple linguistic information representation approach. A case study indicates that industry-education integration environment is the most important division of evaluation, with local regulation being the most important evaluation index. The evaluation model is proven to be effective in improving the urban industry-education integration.

Online teaching quality evaluation based on multi-granularity probabilistic linguistic term sets

In today’s education industry, online teaching is increasingly becoming an important teaching way, and it is necessary to evaluate the quality of online teaching so as to improve the overall level of the education industry. The online teaching quality evaluation is a typical multi-attribute group decision-making (MAGDM) problem, and its evaluation index can be expressed by linguistic term sets (LTSs) by decision makers (DMs). Especially, multi-granularity probabilistic linguistic term sets (MGPLTSs) produced from many DMs are more suitable to express complex fuzzy evaluation information, and they can not only provide different linguistic term set for different DMs the give their preferences, but also reflect the importance of each linguistic term. Based on the advantages of MGPLTSs, in this paper, we propose a transformation function of MGPLTSs based on proportional 2-tuple fuzzy linguistic representation model. On this basis, the operational laws and comparison rules of MGPLTSs are given. Then, we develop a new Choquet integral operator for MGPLTSs, which considers the relationship among attributes and does not need to consider the process of normalizing the probabilistic linguistic term sets (PLTSs), and can effectively avoid the loss of evaluation information. At the same time, the properties of the proposed operator are also proved. Furthermore, we propose a new MAGDM method based on the new operator, and analyze the effectiveness of the proposed method by online teaching quality evaluation. Finally, by comparing with some existing methods, the advantages of the proposed method are shown.

Evaluating supplier sustainability using fuzzy 2-tuple representation

abstract: In the last years, the participation of suppliers in the development, production, and distribution of products has increased. In this way, the sustainability of an organization depends more and more on these partners. The supplier sustainability assessment must be based on the economic, environmental and social tripod called the triple bottom line (TBL). Due to the recent adoption of qualitative metrics related to environmental and social issues in business management, models that deal with qualitative assessments based on experts’ judgments have been required. This paper proposes a supplier sustainability assessment model based on fuzzy 2-tuple linguistic representation using TBL criteria. A pilot application involving the evaluation of 11 suppliers using 13 criteria was carried out in a company that manufactures agricultural products. The application results indicate that supplier 8 presents the higher performance. The criteria evaluated as the most relevant are delivery schedule and product quality. Due to the use of fuzzy 2-tuple representation, the proposed model presents some advantages over the previous similar models, such as the ability to model quantitative and qualitative criteria as well as categorize the supplier's overall performance into groups defined by fuzzy linguistic terms.

A novel fuzzy credit risk assessment decision support system based on the python web framework

ABSTRACT When a financial institute constructs a credit risk assessment model, qualitative, and quantitative data must be considered at the same time due to differences in the attributes of each form of data. While qualitative data are usually relied on professional appraisers’ judgment, 2-tuple fuzzy linguistic representation could assist to organize appraisers’ scores to avoid any loss of information. In practice, a financial institute needs to regularly update its credit risk assessment model to maintain correct assessment results. However, every update involves a lot of numerical experiments using multiple systems or even software packages for evaluating the effect of different sampling methods and classifiers to construct a suitable model for the updated dataset. Such an assessment process is time-consuming with many repetitive processes. This study applied the latest web-based technology to develop a fuzzy decision support system (DSS) that used logistic regression as the classifier combined with different sampling methods and model threshold settings to make data preprocessing and model fitting process more structured and efficient. This DSS was written by Django, a Python web framework, using the RESTful architecture that has good database imaging mechanism and good interaction between the front-end (user side) and the back-end (service side). After verification of multiple actual data set, the average time spent on constructing one model combination per dataset is less than 1 min, which indeed is significantly shorter than the original time required for credit risk assessment and provided a practical tool for the related work of credit risk assessment.

A novel solution approach for multiobjective linguistic optimization problems based on the 2-tuple fuzzy linguistic representation model

This paper proposes a novel solution technique for the multi-objective linguistic optimization problems (MOLOPs) based on the 2-tuple fuzzy linguistic approach. The proposed approach has two main advantages. First, it can handle the MOLOPs in which the linguistic information are represented through either monotonic or non-monotonic functions. Second, for both the scenarios, it provides unique solutions in the linguistic form. On the other hand, the existing MOLOP solution approach which is based on the Tsukamoto’s inference method, provides unique solutions only for those MOLOPs in which the linguistic information are expressed as monotonic functions. For the MOLOPs, in which the linguistic information are expressed as non-monotonic functions, the Tsukamoto’s inference method based solution approach cannot provide unique solutions. Moreover, for both monotonic and non-monotonic cases, the Tsukamoto’s inference method based solution approach cannot provide linguistic solutions, but gives only numeric solutions. We have demonstrated the applicability of the proposed MOLOP solution approach considering a case study on student’s performance evaluation, and compared the results with the Tsukamoto’s inference method based solution approach. It is observed that the proposed approach is capable of addressing the limitations of the Tsukamoto’s inference method and hence is more suitable in solving MOLOPs.

Developing a new BWM-based GMAFMA approach for evaluation of potential risks and failure modes in production processes

PurposeNowadays, the risk assessment and reliability engineering of various production processes have become an inevitable necessity. Because if these risks are not going to be evaluated and no solution is going to be taken for their prevention, managing them would be really hard and costly in case of their occurrence. The importance of this issue is much higher in producing healthcare products due to their quality's direct impact on the health of individuals and society.Design/methodology/approachOne of the most common approaches of risk assessment is the failure mode and effects analysis (FMEA), which is facing some limitations in practice. In this research, a new generalized multi-attribute failure mode analysis approach has been proposed by utilizing the best–worst method and linguistic 2-tuple representation in order to evaluate the production process of hemodialysis solution in a case of Tehran, Iran.FindingsAccording to the results, entry of waste to the mixing tanker, impurity of raw materials and ingredients and fracture of the mixer screw have been identified as the most important potential failures. At last, the results of this research have been compared with the previous studies.Originality/valueSome reinforcement attributes have been added to the traditional FMEA attributes in order to improve the results. Also, the problems of identical weights for attributes, inaccuracy in experts' opinions and the uncertainties in prioritizing the potential failures were improved. Furthermore, in addition to the need for less comparative data, the proposed approach is more accurate and comprehensive in its results.

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

Sağlık Sektöründe Hizmet Kalitesinin Çok Ölçütlü Karar Verme Yöntemleri İle Değerlendirilmesi: Ankara’da Bir Uygulama

Hizmet sektöründe kalite kavramı, üretim sektörüne göre müşterinin algısına çok daha bağımlıdır. Bu da hizmet sunucularının sürekli müşteri odaklı kalite iyileştirme yöntemleri geliştirmesini zorunlu hale getirmektedir. Çok Ölçütlü Karar Verme (ÇÖKV) yöntemleri, önceliklerin ve seçimlerin değerlerinin belirlenmesini sağlayarak hizmet sunucularının kendi alanlarıyla ilgili kalite iyileştirme çalışmalarına katkıda bulunmaktadır. Bu çalışmada, ÇÖKV yöntemleri ile Ankara ilindeki hastane türlerinin hizmet kaliteleri değerlendirilmiştir. Hizmet yararlanıcılarının görüşleri anket yardımı ile toplanmıştır. Ankette yer alan kriterlerin ağırlıklarının belirlenmesi için ise Analitik Hiyerarşi Prosesi (AHP)‟nden yararlanılmıştır. Kriter ağırlıkları ile anket cevapları, 2-Elemanlı Dilsel Temsil Modeline Dayalı Bulanık TOPSIS için veri olarak kullanılmıştır. Bu yöntem ile Ankara'da yer alan hastane türleri (özel, eğitim ve araştırma, devlet ve üniversite) hizmet kalitelerine göre sıralanmıştır. Ankara'da bulunan özel hastanelerin diğer hastane türlerinden daha kaliteli hizmet sunduğu sonucuna ulaşılmıştır.

Integrating the 2-tuple linguistic representation and soft set to solve supplier selection problems with incomplete information

With the advent of the information age, choosing suitable suppliers has become an important issue in supply chain management. Selection of the optimal supplier will ensure the competitiveness and customer satisfaction of the overall supply chain. However, assessment attribute values are sometimes missing or nonexistent in supplier selection. Moreover, experts often fail to confirm the rating values of assessment attributes, hesitating between linguistic term sets. Conversely, because experts have disparate expertise, experience, and backgrounds, they will use different linguistic term sets to evaluate the rating values of assessment attributes. These issues complicate supplier selection. To overcome these hurdles, this paper integrates the 2-tuple linguistic representation and soft set to solve supplier selection problems with incomplete information. To illustrate the application of the proposed approach, a real-life example of the selection of key anesthetic equipment is adopted. This paper also compares the results of this simulation with those of the arithmetic average, fuzzy VIKOR, and interval 2-tuple linguistic VIKOR methods. The proposed approach is more suitable and more accurate in solving supplier selection problems with uncertain and incomplete information.

Computing With Comparative Linguistic Expressions and Symbolic Translation for Decision Making: ELICIT Information

Many real-world decision making (DM) problems present changing contexts in which uncertainty or vagueness appear. Such uncertainty has been often modeled based on the linguistic information by using single linguistic terms. Dealing with linguistic information in DM demands processes of computing with words whose main characteristic is to emulate human beings reasoning processes to obtain linguistic outputs from linguistic inputs. However, often single linguistic terms are limited or do not express properly the expert's knowledge, being necessary to elaborate richer linguistic expressions easy to understand and able to express greater amount of knowledge, as it is the case of the comparative linguistic expressions based on hesitant fuzzy linguistic terms sets. Nevertheless, current computational models for comparative linguistic expressions present limitations both from understandability and precision points of view. The 2-tuple linguistic representation model stands out in these aspects because of its accuracy and interpretability dealing with linguistic terms, both related to the use of the symbolic translation, although 2-tuple linguistic values are still limited by the use of single linguistic terms. Therefore, the aim of this article is to present a new fuzzy linguistic representation model for comparative linguistic expressions that takes advantage of the goodness of the 2-tuple linguistic representation model and improve the interpretability and accuracy of the results in computing with words processes, resulting the so-called extended comparative linguistic expressions with symbolic translation. Taking into account the proposed model, a new computing with words approach is presented and then applied to a DM case study to show its performance and advantages in a real case by comparing with other linguistic decision approaches.