Linguistic Ordered Weighted Averaging Research Articles

Fudge: Fuzzy ontology building with consensuated fuzzy datatypes

An important problem in Fuzzy OWL 2 ontology building is the definition of fuzzy membership functions for real-valued fuzzy sets (so-called fuzzy datatypes in Fuzzy OWL 2 terminology). In this paper, we present a tool, called Fudge, whose aim is to support the consensual creation of fuzzy datatypes by aggregating the specifications given by a group of experts. Fudge is freeware and currently supports several linguistic aggregation strategies, including the convex combination, linguistic OWA, weighted mean and fuzzy OWA, and easily allows to build others in. We also propose and have implemented two novel linguistic aggregation operators, based on a left recursive form of the convex combination and of the linguistic OWA.

A new hybrid supplier selection model

Selecting the right supplier is one of the most challenging tasks for organisations as it essentially reduces purchasing cost and improves corporate competitiveness. This study aims at developing a hybrid model in supplier selection for a non-homogeneous group decision-making process to select a supplier that best satisfies the purchaser. The analytical hierarchy process (AHP), house of quality (HOQ), and linguistic ordered weighted averaging (LOWA) operator are applied in the proposed model. This model is illustrated with a real world example by applying it to a mechanical manufacturing company in Vietnam. It is found that supplier selection does not only depend on a low price offer, but also on supplier's quality, technological capability, capability of on-time delivery, flexibility and good relationship. This study makes new methodological and practical contributions to supplier selection research and applications through development of a hybrid model for non-homogeneous group decision making in supplier selection, and for the first time this study applies the LOWA operator in aggregating linguistic terms of non-homogeneous group decision making in a supplier selection process.

Total orders of extended hesitant fuzzy linguistic term sets: Definitions, generations and applications

Extended hesitant fuzzy linguistic term sets (EHFLTSs) are helpful to model uncertain linguistic information in qualitative group decision making (QGDM). Total orders are essentialwhenever more than two EHFLTSs have to be compared, such as aggregating them by the ordered weighted averaging (OWA) operator. However, the existing studies only focus on partial orders of EHFLTSs and thus their application may be limited. The main purpose of this paper is to develop total orders of EHFLTSs. A constructive approach is proposed to generate total orders by aggregation functions. Three distinct total orders are defined for potential applications as well. To clarify the importance of total orders in QGDM, the extended hesitant fuzzy linguistic OWA operator is developed and some properties are demonstrated. Finally, based on the proposed techniques and the social choice theory, an approach formed by two algorithms is presented to solve the QGDM problems in which some necessary parameters, e.g. risk preferences, are complicated to determine. The proposed total order resolves one current challenge of EHFLTSs and thus can serve as a foundation of decision making with EHFLTSs.

Fuzzy linguistic approach to quality assessment model for electricity network infrastructure

In the modern service economy, the consumer satisfaction is one of primary objectives that a company aims at achieving. Successful companies offer high quality of products or services in order to meet the consumers’ expectation, and, at the same time, they safeguard their own profits and increase market competitiveness. The consumer satisfaction is an indicator to estimate how likely a customer will make a purchase in the future and it is used as a metric very useful in managing and monitoring the company businesses. To address this issue, we present QuAM (Quality Assessment Model), a model for evaluating the overall quality and value of services supplied by a company, through the analysis of the consumer satisfaction. The quality is measured indeed, by the definition of some subjective criteria that are collected through a question form filled in by the consumers. The consumers’ judgments about the supplied items/services allow evaluating the reputation as well as the success of a company. In this work, QuAM has been applied in the electricity network domain, in order to assess an electricity company. In this domain, the overall evaluation of the organization is based on measuring service quality in terms of response times and cost. The quality of service often comes at a cost, with a concern that the pursuit of profit incentives by utilities may have a negative effect on the quality of service. The role of customers is crucial to estimate a market demand curve for service quality, and maximize the customers satisfaction means increasing profitability, productivity and the corporate image. QuAM has been designed by exploiting a fuzzy linguistic approach along with the Computing with Words (CWW) paradigm: the customers feedbacks are modeled through linguistic labels, which naturally fit to describe human judgments; then a linguistic operator LOWA (Linguistic Ordered Weighted Averaging) allows aggregating all the collected judgments into a synthetic linguistic expression. Finally, heuristic measures enable a comprehensive company evaluation.

Group decision making based on induced uncertain linguistic OWA operators

Based on the extended triangular conorm, some new operational laws of linguistic variables are defined. Then a class of new linguistic aggregation operators is developed by extending the OWA operator to the linguistic environment, the uncertain linguistic ordered weighted averaging (ULOWAf) operator, the induced uncertain linguistic ordered weighted averaging (IULOWAf) operator and the uncertain linguistic weighted averaging (ULWAf) operator. Later some desirable properties of the extended operators are studied and a practical group decision making methodology for an uncertain linguistic environment is proposed based on the ULWAf and the IULOWAf operators. A numerical example is given to illustrate the new approach for a financial decision making problem.

On weighted unbalanced linguistic aggregation operators in group decision making

We generalize linguistic evaluation values and their weights in group decision-making (GDM) problems based on unbalanced linguistic terms. The GDM problems include two types of weights: belief degrees of linguistic evaluation values and experts’ weights. Due to the time pressure and conflict of multi-source of information, etc., experts may have various evaluations values on alternatives. The belief degree is used to represent the confidence level of the values. The experts’ weight is used to represent the differences among experts’ importance which caused by experts’ experience or knowledge distinction. We propose the weighted unbalanced linguistic aggregation operators to synthesize linguistic evaluation value, belief degree and experts’ weights. Some desired properties of the operator are then studied, these properties show that the operator extends the linguistic weighted averaging operator (LWA) and the linguistic ordered weighted averaging (LOWA) operator. Finally, an illustrative example of human resource performance appraisal based on the linguistic aggregation operator is provided.

Generalization of the linguistic aggregation operator and its application in decision making

A generalization of the linguistic aggregation functions (or operators) is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean (LWGM) and the linguistic generalized ordered weighted averaging (LGOWA) operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average (WA) and in the ordered weighted averaging (OWA) function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean (LGM), the linguistic OWA (LOWA) operator and the linguistic ordered weighted quadratic averaging (LOWQA) operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.

Induced and Linguistic Generalized Aggregation Operators and Their Application in Linguistic Group Decision Making

We introduce a wide range of induced and linguistic generalized aggregation operators. First, we present the induced linguistic generalized ordered weighted averaging (ILGOWA) operator. It is a generalization of the OWA operator that uses linguistic variables, order inducing variables and generalized means in order to provide a more general formulation. One of its main results is that it includes a wide range of linguistic aggregation operators such as the induced linguistic OWA (ILOWA), the induced linguistic OWG (ILOWG) and the linguistic generalized OWA (LGOWA) operator. We further generalize the ILGOWA operator by using quasi-arithmetic means obtaining the induced linguistic quasi-arithmetic OWA (Quasi-ILOWA) operator and by using hybrid averages forming the induced linguistic generalized hybrid average (ILGHA) operator. We also present a further extension with Choquet integrals. We call it the induced linguistic generalized Choquet integral aggregation (ILGCIA). We end the paper with an application of the new approach in a linguistic group decision making problem.

Developing countries' energy needs and priorities under a sustainable development perspective: A linguistic decision support approach

Sustainable Development (SD) has been discussed extensively in the theoretical literature since the concept was adopted as an overarching goal of economic, social development and environmental protection. The relationship between SD and climate change is of particular importance to developing countries due to their development process and the particular climate challenges that many of them face. The notion is that strategies pursuing SD and climate change mitigation can be mutually reinforcing. In the frame of climate change, the transfer of low-carbon sustainable technologies could ideally provide a framework of helping developing countries to find new sustainable development paths. The aim of this paper is to present a Linguistic Decision Support Approach, based on the Linguistic Ordered Weighted Averaging (LOWA) operator, with the objective of assessing developing countries' energy service needs and priorities. In our approach, a range of energy services and criteria for the assessment were defined, related stakeholders were also involved so as to identify energy service needs and priorities that are most applicable to the country's sustainable development. In this respect, the approach followed could enhance the so far applied Technology Needs Assessments (TNA) and establish a clear understanding of the host country's overall medium to long-term energy and environmental strategy. The application of the particular approach to cross representative developing countries around the world indicated energy efficiency in industry and electricity for industry as high priority energy services.

Some properties of linguistic preference relation and its ranking in group decision making

The group decision making problem with linguistic preference relations is studied. The concept of additive consistent linguistic preference relation is defined, and then some properties of the additive consistent linguistic preference relation are studied. In order to rank the alternatives in the group decision making with the linguistic preference relations, the weighted average is first utilized to combine the group linguistic preference relations to one linguistic preference relation, and then the transformation function is proposed to transform the linguistic preference relation to the multiplicative preference relation, and thus the Saaty's eigenvector method (EM) of multiplicative preference relation is utilized to rank the alternatives in group decision making with the linguistic preference relations. Finally, an illustrative numerical example is given to verify the proposed method. A comparative study to the linguistic ordered weighted averaging (LOWA) operator method is also demonstrated.

A fuzzy linguistic model to evaluate the quality of Web sites that store XML documents

The development of tools to find quality information on the Web is currently a pressing need. The aim of this paper is to present an evaluation model based on fuzzy computing with words to measure the information quality of Web sites that store XML ( eXtensible Markup Language) documents. This model evaluates the information quality of Web sites using only users’ perceptions, and therefore it is user-centered. Fuzzy linguistic techniques are involved in the quality evaluation process to create a user-friendly framework. This model is composed of two main components, an evaluation scheme to analyze the information quality of Web sites and a computing method of quality ratings of Web sites. The evaluation scheme presents both technical criteria related to the Web site characteristics, and criteria related to the content of XML documents stored in the Web sites. The quality ratings represent the ability of Web sites to meet user requirements. Linguistic quality ratings are obtained by combining linguistic evaluation judgements provided by Web visitors on the different evaluation criteria. The computing method is based on two operators for fuzzy computing with words, the LOWA ( Linguistic Ordered Weighted Averaging) operator and the LWA ( Linguistic Weighted Averaging) operator. The later allows to manage relative importance degrees among quality criteria in the evaluation process. This model uses the power of XML Schema language to improve the representation of documents in the Web with semantic characteristics related to their quality and thus it is useful to search quality resources in XML format. Web site quality ratings could be used by Web retrieval systems to help users to find the highest quality XML resources for their information needs. Additionally, this model could be helpful to Web developers to improve the quality of Web sites from a user’s point of view.

On generalized induced linguistic aggregation operators

In this paper, we define various generalized induced linguistic aggregation operators, including generalized induced linguistic ordered weighted averaging (GILOWA) operator, generalized induced linguistic ordered weighted geometric (GILOWG) operator, generalized induced uncertain linguistic ordered weighted averaging (GIULOWA) operator, generalized induced uncertain linguistic ordered weighted geometric (GIULOWG) operator, etc. Each object processed by these operators consists of three components, where the first component represents the importance degree or character of the second component, and the second component is used to induce an ordering, through the first component, over the third components which are linguistic variables (or uncertain linguistic variables) and then aggregated. It is shown that the induced linguistic ordered weighted averaging (ILOWA) operator and linguistic ordered weighted averaging (LOWA) operator are the special cases of the GILOWA operator, induced linguistic ordered weighted geometric (ILOWG) operator and linguistic ordered weighted geometric (LOWG) operator are the special cases of the GILOWG operator, the induced uncertain linguistic ordered weighted averaging (IULOWA) operator and uncertain linguistic ordered weighted averaging (ULOWA) operator are the special cases of the GIULOWA operator, and that the induced uncertain linguistic ordered weighted geometric (IULOWG) operator and uncertain LOWG operator are the special cases of the GILOWG operator.

Induced uncertain linguistic OWA operators applied to group decision making

The ordered weighted averaging (OWA) operator was developed by Yager [IEEE Trans. Syst., Man, Cybernet. 18 (1998) 183]. Later, Yager and Filev [IEEE Trans. Syst., Man, Cybernet.--Part B 29 (1999) 141] introduced a more general class of OWA operators called the induced ordered weighted averaging (IOWA) operators, which take as their argument pairs, called OWA pairs, in which one component is used to induce an ordering over the second components which are exact numerical values and then aggregated. The aim of this paper is to develop some induced uncertain linguistic OWA (IULOWA) operators, in which the second components are uncertain linguistic variables. Some desirable properties of the IULOWA operators are studied, and then, the IULOWA operators are applied to group decision making with uncertain linguistic information.

ON GROUP DECISION MAKING UNDER LINGUISTIC ASSESSMENTS

In this paper we consider a fuzzy logic-based model in group decision making, with a focus on the set of all alternatives and on the individual lingustic preference relations. Some choice processes are devoted to the model using consensus measures and linguistic ordered weighted averaging (LOWA) operator. A multiple criteria group decision model in linguistic setting and some aggregation processes are also considered. The model and the new processes allow to incorporate human consistency in decision support systems.

Choice processes for non-homogeneous group decision making in linguistic setting

Several choice processes of alternatives for non-homogeneous group decision-making problems, assuming linguistic preference relations for expressing the opinions of individuals and linguistic values for expressing their respective power or importance degrees, are presented. These processes are designed using two choice degrees of alternatives based on the concept of fuzzy majority: quantifier guided linguistic dominance degree and quantifier guided linguistic non-dominance degree, being the last one a generalization of Orlovski's non-dominated alternative concept. In order to deal with non-weighted linguistic information, the linguistic ordered weighted averaging (LOWA) operator is applied. To deal with weighted linguistic information, three operators of linguistic weighted information aggregation are used: the linguistic weighted disjunction (LWD) operator, the linguistic weighted conjunction (LWC) operator and the linguistic weighted averaging (LWA) operator.

Direct approach processes in group decision making using linguistic OWA operators

In a linguistic framework, several group decision making processes by direct approach are presented. These processes are designed using the linguistic ordered weighted averaging (LOWA) operator. To do so, first a study is made of the properties and the axiomatic of LOWA operator, showing the rationality of its aggregation way. And secondly, we present the use of LOWA operator to solve group decision making problems from individuals linguistic preference relations.