2-tuple Linguistic Representation Model Research Articles

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.

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.

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.

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.

A note on “Picture 2-tuple linguistic aggregation operators in multiple attribute decision making”

By combining picture fuzzy set and 2-tuple linguistic representation model, Wei et al. (Soft Comput 22(3):989–1002, 2018) proposed the concept of picture 2-tuple linguistic set and presented some operational laws of picture 2-tuple linguistic numbers (P2TLNs). On the basis of operational laws of P2TLNs, some picture 2-tuple linguistic aggregation operators have been further developed. However, some operational laws and results derived by aggregation operators proposed by Wei et al. (Soft Comput 22(3):989–1002, 2018) are conflict with definition of picture 2-tuple linguistic number (P2TLN). In this short note, we propose some novel operational laws of P2TLNs and develop one adjusted picture 2-tuple linguistic aggregation operator, i.e., the adjusted picture 2-tuple linguistic weighted averaging operator.

A 2-tuple integrated DEA-based approach for neuromarketing technology evaluation

Purpose Neuromarketing, which is an interdisciplinary area, concentrates on evaluating consumers’ cognitive and emotional reactions to different marketing stimuli. In spite of advantages, neuromarketing still requires development and lacks a strong theoretical framework. Techniques that are used in neuromarketing studies have different superiorities and limitations, and thus, there is a need for the evaluation of the relevance of these techniques. The purpose of this study is to introduce a novel integrated approach for the neuromarketing research area. Design/methodology/approach The proposed approach combines 2-tuple linguistic representation model and data envelopment analysis to obtain the most efficient neuromarketing technique. It is apt to handle information provided by using both linguistic and numerical scales with multiple information sources. Furthermore, it allows managers to deal with heterogeneous information, without loss of information. Findings The proposed approach indicates that functional magnetic resonance imaging (fMRI) is the best performing neuromarketing technology. Recently, fMRI has been widely used in neuromarketing research. In spite of its high cost, its main superiorities are improved spatial and temporal resolutions. On the other hand, transcranial magnetic stimulation (TMS) and positron emission tomography (PET) are ranked at the bottom because of their poor resolutions and lower willingness of participants. Originality/value This paper proposes a common weight data envelopment analysis (DEA)-based decision model to cope with heterogeneous information collected by the experts to determine the best performing neuromarketing technology. The decision procedure enables the decision-makers to handle the problems of loss of information and multi-granularity by using the fusion of 2-tuple linguistic representation model and fuzzy information. Moreover, a DEA-based common weight model does not require subjective experts’ opinions to weight the evaluation criteria.

Risk Identification and Analysis for PPP Projects of Electric Vehicle Charging Infrastructure Based on 2-Tuple and the DEMATEL Model

Risk management is critical to the success of electric vehicle charging infrastructure public–private partnership (EVCI-PPP) projects, as risks are present throughout the whole life cycle of projects. However, in EVCI-PPP projects, risk factors are often interdependent and, consequently, the interrelationships among factors affect the risk management, which is ignored in the existing studies. To identify the risk factors of EVCI-PPP projects and analyze their internal influence relations, this paper develops a risk identification and analysis model of EVCI-PPP projects based on the 2-tuple linguistic representation model and the decision-making trial and evaluation laboratory (DEMATEL) model. First, a risk factor set is established including 22 criteria involved in 5 dimensions of political/legal risk, economic/market risk, social/environment risk, project/technical risk, and managing risk. Next, the 2-tuple model is introduced to integrate the decision makers’ evaluation information in a linguistic environment, and the direct relation matrix is calculated. Then, the cause–effect relations and a significant degree of risk factors are interpreted using the extended DEMATEL technique. The results show that economic/market risk is the most significant factor of EVCI-PPP projects, and 22 criteria are classified into 14 cause factors and 8 effect factors. Finally, suggestions are provided for decision-makers to ensure the success of EVCI-PPP projects.

An outranking approach for hesitant 2-tuple linguistic sets

The hesitant 2-tuple linguistic set (H2TLS), a special case of hesitant 2-tuple linguistic representation model, not only can express the qualitative preferences of the decision makers (DMs), but can effectively reflect their uncertainty and hesitancy with the help of translation parameter containing more than one element assigned to each linguistic variable of the linguistic term set (LTS). This paper develops an outranking method to address the solution to multi-criteria group decision-making (MCGDM) problems in the context of hesitant 2-tuple linguistic information. The main contribution of this paper is twofold. One is to propose the outranking relations of H2TLEs based on the proposed directional Hausdorff distance. Another is to establish an outranking method, similar to the ELECTRE method, to solve MCGDM problems in which attributes are evaluated by hesitant 2-tuple linguistic arguments under certain criteria. Last but not the least, a numerical example and a comparison analysis of the results obtained both in the proposed method and fuzzy TOPSIS (Technique for order preference by similarity to ideal solution) method for H2TLSs based on the generalized distance measure are conducted to verify the practicality and effectiveness of the work.

A Multicriteria Decision-Making Approach Based on Fuzzy AHP with Intuitionistic 2-Tuple Linguistic Sets

In the modern literature related to linguistic decision-making, the 2-tuple linguistic representation model and its useful applications in various fields have been extensively studied and used during the last decade. Recently, some useful multicriteria decision-making (MCDM) methods have been introduced based on fuzzy analytic hierarchy process (AHP) for 2-tuple linguistic representation model. By keeping in mind the importance of this linguistic model, in this paper, we introduce a fuzzy AHP methodology for intuitionistic 2-tuple linguistic sets (I2TLSs) which is a useful extension of the 2-tuple linguistic representation model. This study is comprised of four stages. In the first stage, we define some operational laws for I2TL elements (I2TLEs) and prove some related important properties. In the second stage, intuitionistic 2-tuple linguistic preference relation (I2TLPR) and multiplicative I2TLPR are defined using I2TLSs. In the 3rd stage, a transformation mechanism is introduced which can transform an I2TLPR to a corresponding intuitionistic preference relation (IPR) and vice versa. In the fourth stage, an approach is proposed for checking the consistency of an I2TLPR and presented a method to repair the inconsistent one by using the proposed transformation mechanism. Finally, a numerical example is given and comparative analysis is carried out with the TOPSIS method to verify the validity of the proposed method.

A Novel Approach Based on Kano Model, Interval 2-Tuple Linguistic Representation Model, and Prospect Theory for Apperceiving Key Customer Requirements

The precisely perception of key customer requirements (CRs) is critically important for customer collaborative product innovation (CCPI) design. A novel approach is proposed based on the Kano model, interval 2-tuple linguistic representation model, and prospect theory. First of all, a Kano model is constructed to preliminarily screen the relatively important product function attributes. For the uncertain and vague information of CRs, an interval 2-tuple linguistic representation model is proposed to determine the weight of CRs. Then, the comprehensive prospects value is utilized for sorting the innovative programs based on the prospect theory. Finally, a numerical example is given to verify the scientific and validity of the proposed method.

Several Generalized Interval-Valued 2-Tuple Linguistic Interval Distance Measures and Their Application

Interval-valued linguistic variables are efficient tools to express the decision makers’ uncertain qualitative judgments. Considering the application of interval-valued linguistic variables, this paper proposes an interval distance measure, which is then used to define interval-valued linguistic interval distance measures by combining the 2-tuple linguistic representation model. To reflect the interactions between elements in a set, three correlative interval distance measures on intervalvalued linguistic variables are proposed. Meanwhile, several models designed to obtain the optimal weighting vector are constructed. After that, an approach to pattern recognition and to multi-attribute decision making with interval-valued linguistic information is developed. Meanwhile, associated examples are offered to demonstrate the concrete application of the proposed procedure.

An extended 2-tuple linguistic DEA for solving MAGDM problems considering the influence relationships among attributes

The main purpose of this paper is to propose an extended data envelopment analysis (DEA) approach for solving multiple attribute group decision making (MAGDM) problems with unknown decision maker (DM) weights and attribute weights. The 2-tuple linguistic representation model is more suitable to deal with linguistic evaluations than the extension principle and the symbolic method. So we propose a 2-tuple linguistic DEA model and objective DM weights determination method in 2-tuple MAGDM problems. The DMs’ objective weights are dynamic with respect to each attribute over different alternatives, which are determined according to the grey relational degree of the decision information DM given with the ideal decision information obtained by solving a weighted least square optimization model. The optimized attribute weights based on the DEA model only reflect the beneficial side to each alternative and ignore the objective side like influence relationships among attributes. Therefore, a 2-tuple linguistic DEMATEL technique is presented and applied to adjust the attribute weights obtained by DEA, and then the comprehensive attribute weights are derived. Finally, the evaluation values of alternatives are obtained based on the comprehensive attribute weights according to the efficiency evaluation method of DEA. A case study is put forward to validate the effectiveness of the proposed approach.

Connecting the linguistic hierarchy and the numerical scale for the 2-tuple linguistic model and its use to deal with hesitant unbalanced linguistic information

The 2-tuple linguistic representation model is widely used as a basis for computing with words (CW) in linguistic decision making problems. Two different models based on linguistic 2-tuples (i.e., the model of the use of a linguistic hierarchy and the numerical scale model) have been developed to address term sets that are not uniformly and symmetrically distributed, i.e., unbalanced linguistic term sets (ULTSs). In this study, we provide a connection between these two different models and prove the equivalence of the linguistic computational models to handle ULTSs. Further, we propose a novel CW methodology where the hesitant fuzzy linguistic term sets (HFLTSs) can be constructed based on ULTSs using a numerical scale. In the proposed CW methodology, we present several novel possibility degree formulas for comparing HFLTSs, and define novel operators based on the mixed 0–1 linear programming model to aggregate the hesitant unbalanced linguistic information.

Some Generalized Interval-Valued 2-Tuple Linguistic Correlated Aggregation Operators and Their Application in Decision Making

With respect to multi-attribute decision making under uncertain linguistic environment, a new interval-valued 2-tuple linguistic representation model is introduced. To deal with the situation where the elements in a set are interdependent, several generalized interval-valued 2-tuple linguistic correlated aggregation operators are defined. It is worth pointing out that some interval-valued 2-tuple linguistic operators based on additive measures are special cases of our operators. Meanwhile, several special cases and desirable properties are discussed. Furthermore, models based on the correlation coefficient are constructed, by which the optimal weight vector can be obtained. Moreover, an approach to multi-attribute group decision making with uncertain linguistic information is developed. Finally, an example is selected to show the effectivity and feasibility of the developed procedure.

How “OPTIMUS” is a city in terms of energy optimization? e-SCEAF: A web based decision support tool for local authorities

Nowadays cities tend to become “Smarter”, usually disregarding the issues of energy efficiency and sustainability. Therefore, optimizing energy use in a city remains a challenge and respective decision support systems are important to guide local authorities toward that direction. This paper provides a holistic approach presenting a Smart City Energy Assessment Framework (SCEAF) along with a specific web based decision support tool, the so-called e-SCEAF, which can provide local authorities with fruitful results for assessing the energy behavior and performance of their city. The tool merges heterogeneous information, such as clearly quantifiable energy related indicators, the related city policy context performance and the integration of smart infrastructure. This multi-source information fusion is based on the 2-tuple linguistic representation model of Herrera and Martínez. This particular model has been widely used in decision problems and was mainly selected due to the fact that it provides linguistic results that are accurate and easy to understand by the cities’ local authorities. The performance, usefulness and effectiveness of the SCEAF framework and the e-SCEAF tool are tested on a real life application in three different cities, Savona (Italy), Sant Cugat del Vallès (Spain) and Zaanstad (The Netherlands). In this respect, the role of fusion methods and algorithms for merging multiple information will be evaluated in a “real life environment”.

A web tool to support decision making in the housing market using hesitant fuzzy linguistic term sets

In this paper we present a linguistic multiple-expert multi-criteria decision making model and a web tool to support it, that is centred on the housing market. The web tool is integrated with the usual catalogue of resources for rental or for sale, enriched with the possibility of ranking a subset of properties according to the client's preferences and the internal knowledge associated to the properties. Usually the description of a property is quantitative, thought in our case we add qualitative information corresponding to assessments made by housing agents. These agents are considered experts in the market conditions.We apply the 2-tuple linguistic representation model to keep accuracy in the processes of Computing with Words and the hesitant fuzzy linguistic term sets to qualify in situations of uncertainty and hesitation in the assessments. The software helps the agents in the process of the elicitation of the linguistic expression based on the fuzzy linguistic approach and the use of context-free grammars, and the web clients in the decision of visiting a property.

An integrated fuzzy MCDM approach for supplier evaluation and selection

A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.

An investment evaluation of supply chain RFID technologies: A group decision-making model with multiple information sources

Selection of radio frequency identification (RFID) technology is important to improving supply chain competitiveness. The objective of this paper is to develop a group decision-making model using fuzzy multiple attributes analysis to evaluate the suitability of supply chain RFID technology. Since numerous attributes have been considered in evaluating the RFID technology suitability, most information available in this stage exhibits imprecise, subjective and vague. Fuzzy set theory appears as an essential tool to provide a decision framework for modeling imprecision and vagueness inherent in the RFID technology selection process. In this paper, a fuzzy multiple attributes group decision-making algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and maximum entropy ordered weighted averaging operator is developed. The proposed method is apt to manage evaluation information assessed using both linguistic and numerical scales in group decision making problem with multiple information sources. The aggregation process is based on the unification of fuzzy information by means of fuzzy sets on a basic linguistic term set. Then, the unified information is transformed into linguistic 2-tuple in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The proposed method can facilitate the complex RFID technology selection process and consolidate efforts to enhance group decision-making process. Additionally, this study presents an example using a case study to illustrate the availability of the proposed method and its advantages.

Multi-criteria group decision making method based on interval 2-tuple linguistic information and Choquet integral aggregation operators

Considering that interval 2-tuple linguistic model can solve the interval linguistic multi-criteria decision-making problems, and Choquet integral can deal with the multi-criteria group decision-making problems with correlated criteria, in the revelation of these two methods, this paper combined them together to cope with the interval linguistic multi-criteria group decision-making problems with correlated criteria. Because of the limitations of 2-tuple linguistic representation model, a modified composite scale based on 2-tuple linguistic model was proposed which can better improve the accuracy of decision-making. First, using Choquet integral and interval 2-tuple linguistic model, two aggregation operators with their properties were introduced to better reflect correlations among criteria, i.e., the interval 2-tuple correlated averaging operator and the interval 2-tuple correlated geometric operator. Then, a multi-criteria group decision-making method based on the above operators was proposed. Finally, the proposed method was compared with the existing ones based on the same illustrative example to confirm its feasibility and rationality.