2-tuple Model Research Articles

A multi-criteria decision support framework for assessing seaport sustainability planning: the case of Piraeus

ABSTRACT Seaports will play a pivotal role in the low-carbon transition of maritime shipping, the policy landscape in which is currently being shaped. In this context, we introduce a multi-criteria decision support framework for seaport sustainability planning, to identify the competitiveness of interventions under uncertainty and evaluate the direction of the sector’s policy context in terms of required actions. The framework, based on the 2-tuple TOPSIS model, heterogeneous variables, and a Monte Carlo robustness analysis, is empirically applied to the port of Piraeus, Greece, to explore the most competitive interventions and their vulnerability to uncertainties. To inform port authorities and policymakers in the sector, we emphasise the added value of selected, inexpensive actions for energy efficiency and hybrid mobility. Furthermore, we find costly and seemingly obligatory actions under current European legislation, like cold ironing and LNG, to be robust and in the right direction if perception of non-financial risks is reduced.

Profiling clients in the tourism sector using fuzzy linguistic models based on 2-tuples

Customer segmentation is a key piece of a company’s business strategy. This paper presents a segmentation of the online users of tourism platforms through the recency, frequency and helpfulness of the users. 2-tuples model is applied to these variables to be more precise without loss of information. In addition, the functionality of the proposal made by the authors is verified through a use case in which TripAdvisor opinioners are segmented in reference to the experience lived in hotels and tourist accommodation.

Limiting Factors of Extenuating Circumstances Using Neutrosophic 2-Tuple

Every court is responsible for applying the law under the principles of fairness, justice, ethical-legal, and human value because it cannot be forgotten that every criminal is a human being. The foregoing is why the application of extenuating circumstances acquires a certain complexity. Currently, in Ecuador,certain situations cause mismanagement of justice due to a lack of understanding and assimilation of extenuating circumstances. Therefore, this is adopted as a problem situation, establishing as the main objective, to analyze the limiting factors in apply in extenuating circumstances using the 2-Tuple model in its neutrosophic extension.

A geospatial model of RFM analysis: An application to tourism in the Iberian Peninsula

This paper presents a geographic summary of the online users of tourism platforms through the recency, frequency and helpfulness of the users. 2-tuples model is applied to these variables to be more precise without loss of information. In addition, the functionality of the proposal made by the authors is verified through a use case in which TripAdvisor opinioners post their experience with NH Group hotels in the Iberian Peninsula. With this, it is intended to provide a solution to businesses with multiple geographical locations to focus their efforts on those places where their customers are more influential and publish points of improvement of the company.

A type-1 OWA operator for Extended Comparative Linguistic Expressions with Symbolic Translation

Recently, a new representation model has been proposed to improve the interpretability and accuracy of Computing with Words (CW) processes, the so-called Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT). It extends the concept of comparative linguistic expressions based on hesitant fuzzy linguistic term sets into a continuous domain by means of the symbolic translation used in the linguistic 2-tuple model. The aggregation of this kind of information is vital when solving decision making problems, but as the field of ELICIT information is still quite new, not many aggregation operators have been defined for it. Therefore, taking into account the need to deal with the fuzzy representation of ELICIT information for its aggregation, we propose the ELICIT type-1 OWA (ELICIT-t1-OWA) operator based on the equivalent α−level type-1 OWA operator to simplify the CW processes by using ELICIT trapezoidal fuzzy representations. The ELICIT-t1-OWA operator will use interval weights induced by an interval-valued linguistic quantifier to aggregate ELICIT expressions and then its properties will be studied. Additionally, a way to deal with both the weights and arguments of the ELICIT-t1-OWA operator is provided in the form of fuzzy numbers. Finally, a decision making model dealing with ELICIT information that carries out the aggregation by using the proposed ELICIT-t1-OWA operator is developed and a numerical example to show its effectiveness is introduced.

An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business

In the field of strategic marketing, the recency, frequency and monetary (RFM) variables model has been applied for years to determine how solid a database is in terms of spending and customer activity. Retailers almost never obtain data related to their customers beyond their purchase history, and if they do, the information is often out of date. This work presents a new method, based on the fuzzy linguistic 2-tuple model and the definition of product hierarchies, which provides a linguistic interpretability giving business meaning and improving the precision of conventional models. The fuzzy linguistic 2-tuple RFM model, adapted by the product hierarchy thanks to the analytical hierarchical process (AHP), is revealed to be a useful tool for including business criteria, product catalogues and customer insights in the definition of commercial strategies. The result of our method is a complete customer segmentation that enriches the clusters obtained with the traditional fuzzy linguistic 2-tuple RFM model and offers a clear view of customers’ preferences and possible actions to define cross- and up-selling strategies. A real case study based on a worldwide leader in home decoration was developed to guide, step by step, other researchers and marketers. The model was built using the only information that retailers always have: customers’ purchase ticket details.

RETRACTED ARTICLE: A systematic review of developments in the 2-tuple linguistic model and its applications in decision analysis

Several practical problems incorporate uncertainty and erroneous information within its framework of definition. Many distinguished models have already been developed to manage such unreliability, like probabilistic models, and so forth. However, to revitalize the problems where uncertainty is not probabilistic, or the information is vaguely accessible, other tools like fuzzy logic and fuzzy linguistic-based approach have emerged. The efficacy of a linguistic-based approach pertains to a problem of decision making involving qualitative information where precise numerical modeling appears to be inappropriate. The 2-tuple linguistic model formalized by Herrera and Martinez in year 2000, has upgraded several linguistic processes for solving complex decision-making issues. Its successful application in different fields has impelled several researchers to work in its extension. This study aims to offer a systematic literature review on a 2-tuple linguistic model as well as discuss its extensions. The manuscript lays out a brief review of 188 articles published between 2000 and 2019. Based on the 188 selected articles, the following questions can be justified: (1) What is the fuzzy linguistic approach? (2) What is the linguistic model based on the 2-tuples and its foundation? (3) Why the 2-tuple model preferred over other existing classical linguistic models? (4) What are the extensions of the 2-tuple model? Moreover, (5) What are the major applications of a 2-tuple model witnessed in varied disciplines, specifically decision analysis? Therefore, seeking answers to these questions indicate current trends in research. Further, it provides the direction for scholars to work in this field and overcome the challenges associated with the prevailing 2-tuple model.

Evaluation Model of Industrial Operation Quality Under Multi-source Heterogeneous Data Information

Constructing scientific evaluation system and evaluation methods to make timely quantitative evaluation for regional industrial operation quality is of great practical significance for expediting the new industrialization process and promoting the improvement of national economic operation quality. Aiming at the problem of evaluating the industrial operation quality, this paper constructs a new evaluation system from the perspective of industrial operation performance and industrial development potential, and then proposes a multi-source heterogeneous multi-attribute decision-making method based on the linguistic 2-tuple model to evaluate the industrial operation quality. In this method, the original multi-source heterogeneous data whereby real numbers, interval numbers, and linguistic fuzzy numbers coexist are all transformed into linguistic 2-tuples, then a new ranking method based on grey relational degree of linguistic 2-tuple matrix is presented to rank the level of industrial operation quality for the given cities. Further, a decision-making example of evaluating the industrial operation quality for 14 cities in Hunan Province of China is provided to highlight the implementation, availability, and feasibility of the proposed evaluation model.

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.

A comparison between Fuzzy Linguistic RFM Model and traditional RFM model applied to Campaign Management. Case study of retail business.

Recency Frequency Monetary Value (RFM) is a clear and descriptive way to classify a customer database based on purchasing behavior that direct marketers have used with success since almost the last twenty years. Despite the fashion that exists lately around predictive models and artificial intelligence, direct marketing’s RFM, still has a place in modern database marketing. In a real business environment, RFM can still be useful when models are not practical because it is user friendly and the outcome is always interpretable. It can also be used to combine with other models. In this paper, we show a real example about how easy, accurate and explainable can be a customer segmentation based on the traditional RFM model and the 2-tuple RFM model applied to a customer database. It will help us to better understand the benefits of applying the 2-tuple model instead of the traditional one. We will be able to see how, by applying a k-means clustering on top of the 2-tuple model, segments have a great applicability from the business point of view. By using descriptive variables, we will clarify the cluster description and the model will provide us an extremely clear idea about how customers behave. The main goal for developing this example was to define the best target for a direct campaign communication. Data used for this analysis belongs to a worldwide home furniture, Scandinavian Retailer, and are related to its loyalty program which give us the members’ historical purchase information.

A MODEL TO OBTAIN A SERVPERF SCALE EVALUATION OF THE CRM CUSTOMER COMPLAINTS: AN APPLICATION TO THE 4G TELECOMMUNICATIONS SECTOR

The relationship between customer churn and their complaints is sufficiently contrasted in the telecom sector. Therefore, a key part of a company’s strategy is the measurement of this dissatisfaction. It is important to conduct periodic surveys on complaints in a standard form like the SERVPERF scale because it enables the organization to benchmark. Many of these complaints are stored in the company’s CRM. Our first aim is to define a model to transform CRM customer complaints, expressed in natural language, into SERVPERF scales. In the proposed model, we use the 2-tuple model, which allows computing this linguistic data without losing information. Our second purpose is to implement a prototype to apply the model in a 4G Company. As a practical conclusion, most complaints in this emerging technology (which still has some deficiencies) are related to technical aspects of the services rather than to staff.

A novel semi-quantitative Fuzzy Cognitive Map model for complex systems for addressing challenging participatory real life problems

Fuzzy Cognitive Maps (FCM) are a promising approach for socio-ecological systems modelling. FCMs represent problem knowledge extracted from different stakeholders in the form of connected factors/variables with imprecise cause-effect relationships and many feedback loops. These typically large maps are condensed and aggregated to obtain a summary view of the system. However, representation, condensation and aggregation of previous FCM models are qualitative due to lack of appropriate quantitative methods. This study tackles these drawbacks by developing a semi-quantitative FCM model consisting of robust methods for adequately and accurately representing and manipulating imprecise data describing a complex problem involving stakeholders for pragmatic decision making. The model starts with collecting qualitative imprecise data from relevant stakeholders. These data are then transformed into stakeholder perceptions/FCMs with different causal relationship formats (linguistic or numeric) which the proposed model then represents in a unified format using a 2-tuple fuzzy linguistic representation model which allows combining imprecise linguistic and numeric values with different granularity and/or semantic without loss of information. The proposed model then condenses large FCMs using a semi-quantitative method that allows multi-level condensation. In each level of condensation, groups of similar variables are subjectively condensed and the corresponding imprecise connections are computationally condensed using robust calculations involving credibility weights assigned to variables (variables’ importance). The model then uses a quantitative fuzzy method to aggregate perceptions/FCMs into a stakeholder group or social perception/FCM based on the 2-tuple model and credibility weights assigned to FCMs (stakeholders’ importance). Thereafter, the structure of produced FCMs is analysed using graph theory indices to examine differences in perceptions between stakeholders or groups. Finally, the model applies various what-if policy scenario simulations on group FCMs using a dynamical systems approach with neural networks and analyses scenario outcomes to provide appropriate recommendations to decision makers. An example application illustrates method’s effectiveness and usefulness.

A linguistic fusion approach for heterogeneous Environmental Impact Significance Assessment

Environmental Impact Significance Assessment (EISA) is usually modeled as a multi-criteria decision making process for determining the importance of project’s impacts over involved environment, considering subjective judgments provided in a qualitative and/or quantitative way. Classical EISA methods are not efficient in handling heterogeneous contexts since experts are forced to use numerical scales even for assessing subjective environmental indicators and they also obtain numerical outputs of low interpretability. In this paper is proposed a new approach for heterogeneous EISA based on the linguistic 2-tuple fusion model for dealing with heterogeneous information. It provides a flexible evaluation framework in which experts can supply their preferences using different information domains conform to the nature and uncertainty of criteria as well as their level of knowledge and experience. Moreover the approach applies a multi-step aggregation process to obtain interpretable significance values without loss of information.

Comparison and links between two 2-tuple linguistic models for decision making

This paper deals with linguistic models that may prove useful for representing the information during decision making.Data extraction is a complex problem, especially when dealing with information coming from human beings (linguistic assertions, preferences, feelings, etc.) and several models have emerged to overcome difficulties in expressing the data. Among those models, we are interested in two of them: the 2-tuple semantic model and the 2-tuple symbolic model.In this paper we stress on a comparison between both models and we prove that links can be made between them. An interesting result is obtained: the 2-tuple semantic model can generate a partitioning that is identical to the one that would be generated thanks to the symbolic 2-tuple model. This permits to compare the models and mix the two when there is a need to use one model or another, depending on the applications, and then to reach a consensus. In closing, an example of the use is given to demonstrate the value of the method.

A 2-tuple Fuzzy Linguistic RFM Model and Its Implementation

Abstract RFM is a model used to analyze the behavior of customer by means of three variables: Recency, Frequency and Monetary. The scores of these variables are expressed by an integer number, typically, in the range 1..5. The fuzzy linguistic approach is a tool intended for modeling qualitative information in a problem. In this paper, we propose to manage these RFM scores using the 2–tuple model which is a fuzzy linguistic model of information representation that carries out processes of “computing with words” without the loss of information. The proposed model permits us an easy linguistic interpretability and let us obtain a more precise representation of the RFM scores. Therefore, by interpreting these linguistic scores, decision makers can effectively identify valuable customers and consequently develop more effective marketing strategy. Additionally, we present an IBM SPSS Modeler implementation of this model. As a particular case study, we show an application example in order to select the customer of a loyalty campaign.

Consistency-driven automatic methodology to set interval numerical scales of 2-tuple linguistic term sets and its use in the linguistic GDM with preference relation.

The 2-tuple linguistic modeling is a popular tool for computing with words in decision making. In order to deal with the linguistic term sets that are not uniformly and symmetrically distributed, the numerical scale model has been developed to generalize the 2-tuple linguistic modeling. In the numerical scale model, the key task of the 2-tuple based models is the definition of a numerical scale function that establishes a one to one mapping between the linguistic information and numerical values. In this paper, we propose a consistency-driven automatic methodology to set interval numerical scales of 2-tuple linguistic term sets in the decision making problems with linguistic preference relations. This consistency-driven methodology is based on a natural premise regarding the consistency of preference relations. If linguistic preference relations provided by experts are of acceptable consistency, the corresponding transformed numerical preference relations by the established interval numerical scale are also consistent. Compared with the existing approach based on canonical characteristic values, the consistency-driven methodology provides a new way to set the interval numerical scale without the need of the semantics defined by interval type-2 fuzzy sets. Meanwhile, interval multiplicative preference relations are used in the pairwise comparisons method and the presented theory can be utilized in the pairwise comparisons method as it provides a novel approach to automatic construct interval multiplicative preference relations. Finally, we present the framework for the use of the consistency-driven automatic methodology in linguistic group decision making problems and two numerical examples are given to illustrate the feasibility and validity of this proposal.

Toward a Classification of Hesitant Operators in the 2-Tuple Linguistic Model

Several types of sets, such as hesitant fuzzy sets, permit to express hesitation, doubt, or perplexity during, e.g., a decision-making process. In this paper, we start giving thought to the hesitant operators in certain models such as the 2-tuple models or the symbolic model. On the basis of the definition of uncertainty, hesitation, doubt, etc. in linguistics, we suggest to shed new light on their meaning and on the way they can be linked in the considered frameworks. Five types of hesitant expressions are discussed, and a parallel between the expressions and the mathematical operators in the considered models is drawn. Two new operators in the 2-tuple semantic model and one new operator in the symbolic model are proposed with a classification of all the discussed operators.

An extension of MACBETH method for a fuzzy environment to analyze alternatives in reverse logistics for automobile tire wastes

Waste tire related environmental problems and its recycling alternatives have been a major issue nowadays because of their complex combination of very different materials, which include several rubbers, carbon blacks, steel cord and other organic and inorganic minor components. The most important problem in the scrap tire recycling program is the type of product recovery option because there are few specific data available. Multi-criteria decision analysis (MCDA) was used to assess options in reverse logistics for waste tire. MCDA is a widely used decision methodology that considers conflicting systems of criteria. However, many real-world decision problems involve ambiguity and imprecise information. In this study, the analysis has been undertaken using an extended version of MACBETH methodology to take into account the imprecise and linguistic assessments provided by a decision-maker by integrating the 2-tuple model dealing with non-homogeneous information data. The proposed fuzzy MACBETH method has been applied to a real case related to the automobile tire waste to elucidate its details.

Managing logistics customer service under uncertainty: An integrative fuzzy Kano framework

A logistics customer service model is a critical competitive advantage that enhances both customer satisfaction and firm performance. Researchers have developed several models for assessing customer requirements, measuring product performance, and positioning products. However, handling customers’ linguistic preferences and uncertain product attributes remain significant and unresolved problems. In this study, we develop an integrative framework that incorporates the Kano model, fuzzy distances, and 2-tuple fuzzy-linguistic model to manage customer-service logistics more effectively. Following a five-module architecture, we consider numerical, fuzzy, and linguistic data on product attributes and customer requirements. We first evaluate product attributes using utility-value functions and converted into satisfaction scores related to Kano categories. We then consider raw importance assessments to obtain an overall satisfaction score for each market and product. An empirical example illustrates the benefits of this integrative approach. The results show that our proposal can effectively manage logistics customer service, enabling managers to identify targets and formulate competitive strategies to enhance customer satisfaction.

Computing the Numerical Scale of the Linguistic Term Set for the 2-Tuple Fuzzy Linguistic Representation Model

When using linguistic approaches to solve decision problems, we need the techniques for computing with words (CW). Together with the 2-tuple fuzzy linguistic representation models (i.e., the Herrera and Martinez model and the Wang and Hao model), some computational techniques for CW are also developed. In this paper, we define the concept of numerical scale and extend the 2-tuple fuzzy linguistic representation models under the numerical scale. We find that the key of computational techniques based on linguistic 2-tuples is to set suitable numerical scale with the purpose of making transformations between linguistic 2-tuples and numerical values. By defining the concept of the transitive calibration matrix and its consistent index, this paper develops an optimization model to compute the numerical scale of the linguistic term set. The desired properties of the optimization model are also presented. Furthermore, we discuss how to construct the transitive calibration matrix for decision problems using linguistic preference relations and analyze the linkage between the consistent index of the transitive calibration matrix and one of the linguistic preference relations. The results in this paper are pretty helpful to complete the fuzzy 2-tuple representation models for CW.