Fuzzy Ordered Weighted Averaging Research Articles

A fuzzy parametric model for decision making involving F-OWA operator with unknown weights environment

Weight determining of attributes is an important factor in decision support systems since it corresponds to the relative importance of each criteria which is necessary to be determined since all the attributes aren't equally important. The aim of this paper is to put forward a method for multi Criteria decision making (MCDM) problems based on three trapezoidal fuzzy numbers under completely unknown weights environment. Based on the idea that the attribute with a larger deviation value among alternatives should be assigned a larger weight, an optimization model based on maximizing deviation method is established. F-OWA is considered to be vastly superior from the existing operators which usually take into account only the relative significance of decision makers. F-OWA operator considers not only the ratings of attribute values but also their ordered position that is it not only signifies the decision makers but also values the individual assessments. We utilize fuzzy ordered weighted averaging (F-OWA) operator to compute the collective overall preference value of each alternative and select the most desirable one according to their expected score values. The presented method is more generalized since we have used TTFNs, which are more effective in capturing uncertainty than IT2FS, just like triangular fuzzy numbers have a better representational power than simple interval numbers. Moreover, an illustrative example is given for the justification of the proposed technique.

A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran

One of the specific features of porphyry copper (Cu) mineralization is the distinct occurrence of hydrothermal alteration zones, which can be mapped by processing various satellite images. Among free multispectral images, Landsat 8 OLI and ASTER data are known to be efficient in mapping different geological features, such as alteration zones and tectonic lineaments. This study aims to show the potential of these data types in mapping porphyry Cu mineralization by proposing a framework for employing and integrating different image processing methods. These methods include principal component analysis (PCA), spectral angle mapper (SAM), and matched filtering (MF) employed on these satellite images to map target al.teration zones. Moreover, PCA and directional filtering are applied to the ASTER dataset to enhance and map structural features. The results are evaluated and then combined to provide a potential map of Cu mineralization in the Chahargonbad area, located within the Urumieh-Dokhtar magmatic belt (UDMB) in Kerman province, Iran. The prediction-area plot and normalized density, which are data-driven methods, are used to assign the relative weight of each layer and evaluate them. Finally, using the calculated weights, data-driven multi-index overlay (DMIO) and fuzzy ordered weighted averaging (FOWA) methods are applied to combine the evidential layers. The potential mineralization maps created by the DMIO and FOWA provide a prediction rate of 80% and 82%, respectively. Furthermore, the accuracy of the integrated maps is investigated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. The AUC scores obtained from the ROC curves of DMIO and FOWA methods are 0.85 and 0.88, respectively, representing powerful positive spatial relationships with mineralization areas. Based on the results, the proposed framework can be applied to provide a potential map of porphyry Cu mineralization, particularly in arid regions, with reasonable accuracy.

Comprehensive modeling of mineral potential mapping by integration of multiset geosciences data

This work has evaluated the functionality of various fuzzy-based fusion methods in the mineral potential mapping (MPM), by which a multi-criteria decision-making problem was solved to design a layout for drilling complementary boreholes through a comprehensive analysis of geospatial datasets. The novel methods employed were fuzzy c-means clustering, fuzzy gamma operator, fuzzy inference system (FIS), fuzzy outranking, and fuzzy ordered weighted averaging (FOWA). Kahang porphyry Cu-Mo deposit in the Isfahan province of Iran was chosen as a case study to examine the performance of these fuzzy methods in MPM. Extracted geospatial indicator layers for assessing the potential of porphyry-type mineralization were derived from four criteria, namely geology (rock units and faults), remote sensing (alteration map), geochemistry (Cu, Mo, and factor maps), and geophysics (reduced to the pole and analytical signal of magnetic data). The concentration-area multifractal method was utilized to reclassify each synthesized fuzzy favorability map into five classes. To appraise and compare the efficiency of each employed method, a productivity measure assumed as a cumulative summation of Cu grade multiplied by its thickness above an economical cut-off value of 0.2% was calculated along with each drilling (totally 33 ones). According to fuzzy favorability maps derived from running all fuzzy methods, the FIS and FOWA had the highest efficiency with 80 and 78% of accuracy, respectively. Eventually, taking all fuzzy maps into account led to the delineation of some new favorable zones, whereby further exploratory investigations are envisioned for determining their mining potential.

Research on the decision-making of flood prevention emergency plans during reservoir construction based on generalized intuitionistic fuzzy soft sets and TOPSIS

Abstract Reservoir engineering is of great significance for the reduction of regional flood disasters and ensuring the sustainable development of agriculture. This paper proposed a decision-making model based on generalized intuitionistic fuzzy soft sets and TOPSIS. First, an evaluation index system was comprehensively identified and constructed. Then, generalized intuitionistic fuzzy soft sets were used to describe the index attribute values of emergency plans to fully reflect the certainty, uncertainty, and hesitancy of indexes, and their weights were calculated by Fuzzy Ordered Weighted Averaging (FOWA) to adequately consider the ambiguity of experts' judgment. Finally, the TOPSIS method was extended via the generalized intuitionistic fuzzy soft sets to the sequencing of emergency plans. In addition, the Wangjiazhou Reservoir Project in China was selected as a case study. The case study demonstrated that full use of emergency materials and personnel was the most important factor, and the plan of the overflow rock-fill dam was the optimal flood prevention emergency plan. Compared with the classical TOPSIS, the new model proposed in this paper was found to have improved feasibility and effectiveness, and its evaluation results were more objective and reasonable. Therefore, the proposed method could provide both theoretical and practical reference.

Participatory modelling for poverty alleviation using fuzzy cognitive maps and OWA learning aggregation.

Participatory modelling is an emerging approach in the decision-making process through which stakeholders contribute to the representation of the perceived causal linkages of a complex system. The use of fuzzy cognitive maps (FCMs) for participatory modelling helps policy-makers develop dynamic quantitative models for strategising development interventions. The aggregation of knowledge from multiple stakeholders provides consolidated and more reliable results. Average aggregation is the most common aggregation method used in FCMs-based modelling for weighted interconnections between concepts. This paper proposes a new aggregation method using learning OWA (ordered weighted averaging) operators for aggregating FCM weights assigned by various stakeholders. Besides, we report a comparative analysis of 'OWA learning aggregation' with the conventional average aggregation method, while evaluating the theory of change for the world's most extensive poverty alleviation programme in India. The results of the FCMWizard web-based tool show that the proposed method provides an opportunity to policy-makers for evaluating outcomes of proposed policies while addressing social resilience and economic mobility.

Study on location decision framework of electric vehicle battery swapping station: Using a hybrid MCDM method

Battery swapping station (BSS) is a new mode of supplying power for electric vehicle (EV). As the pre-stage of BSS construction, location decision problem plays an important role in its whole life cycle, which directly affects the economic profits. However, most of the existing researches are about its operation and management problem. Therefore, this paper proposes a site selection framework for the BSS based on MCDM (Multi-criteria decision making) method. Firstly, three new criteria are considered and the criteria system is established on the basis of existing research. Then, considering the information inadequacies in the decision-making process, triangular fuzzy numbers are adopted to deal with uncertainties. Afterwards, in view of the strong dependence among criteria, the fuzzy DEMATEL (Decision Making Trail and Evaluation Laboratory) method is applied to determine the weights of criteria. And then, the FOWA (Fuzzy ordered Weighted Averaging) operator is adopted to aggregate the evaluation values on alternatives of experts and the fuzzy MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the Full Multiplicative Form) approach is used to rank the alternatives. Finally, a case in Beijing, China with sensitivity analysis and comparison results also illustrate the effectiveness and robustness of the proposed approach. This paper provides a critical tool for investors to select the most appropriate alternative.

Multiple Attribute Group Decision Making Methods Using Numerical Methods of Intuitionistic Triangular Fuzzy Sets

Solving Multiple Attribute Group Decision Making (MAGDM) problems has become one of the most important research in the recent trends. The information or data is in the form of Intuitionistic Triangular Fuzzy Number (ITrFN). The weights are derived from the numerical solutions of partial differential equations of Laplace’s equation and poisson`s equation. The weights obtained from the methods are applied in decision making problems. The Intuitionistic Triangular Fuzzy Ordered Weighted Averaging (ITrFOWA) operator and the Intuitionistic Triangular Fuzzy Hybrid Aggregation (ITrFHA) operator are used to combine the decision matrix. Distance function is used as a tool for ranking the best alternatives. Numerical illustration is proposed to show the effectiveness of the method.

Mobile Phone Recommender System Using Information Retrieval Technology by Integrating Fuzzy OWA and Gray Relational Analysis

With the advancement and diversification of information retrieval technology, such technology has been widely applied in recent years in personalized information recommender systems (RSs) and e-commerce RSs in addition to data-mining applications, especially with respect to mobile phone purchases. By integrating the weights of fuzzy ordered weighted averaging (OWA) and gray relational analysis, this research calculated the recommended F1 indices of three weight calculation methods to be 20.5%, 14.36%, and 16.43% after an examination by 30 experimenters. According to the operational results attained by the 30 experimenters, the recommended products obtained by the fuzzy OWA and gray relational analysis calculation method covered the products recommended by the other two weight calculation methods with a higher recommendation effect.

A linguistic multi-criteria decision-aiding system to support university career services

In this paper we introduce a linguistic multi-criteria decision-aiding model to support college students with the internship job market application. It considers a fuzzy ordered weighted averaging (FOWA) operator in the matching to capture the inherent uncertainty and vague nature of personnel selection processes. The decision model is integrated in a software tool able to capture data from university student resume and internship databases. The application assesses position characteristics implicitly by means of linguistic descriptions according to each student's preferences. The software tool is enabled with the ability to propose positions according to student preferences. The system selects a reduced list of alternatives from the set of job offers, helping students to decide on which positions to focus their applications.

Landfill site selection using combination of fuzzy logic and multi-attribute decision-making approach

During the last decades, growth of urbanization and industrialization led to an increase in solid waste generation. Landfilling is the most prevalent ultimate disposal method for the municipal solid wastes in developing countries. The rapid municipal solid waste generation in Markazi province (central part of Iran) causes the need for precision in finding a suitable landfill site selection. In the present study, 12 factors (environmental and socioeconomic factors) have been applied to select the landfill site in Markazi province, Iran. The different methods including the analytic network process (ANP) combined with fuzzy linguistic quantifier, ordered weighted average (OWA), and weighted linear combination (WLC) approach in geographic information system was applied to find an appropriate landfill site. The OWA operator function permits the evaluation of the wide spectrum of consequences (with different scenario) obtained from different management strategies. Results revealed that integration of fuzzy logic, ANP, and OWA provides flexible and better ideas compared to the Boolean logic and WLC to select a suitable landfill site.

Contrasting Correlation Coefficient with Distance Measure in Interval Valued Intuitionistic Trapezoidal Fuzzy MAGDM Problems

In this paper, Multiple Attribute Group Decision Making (MAGDM) problems in which the data is of the form of Interval Valued Intuitionistic Trapezoidal Fuzzy Numbers (IVITzFNs) is presented. Some operational laws of IVITzFNs are introduced. Then some new aggregation operators including interval valued Intuitionistic Trapezoidal Fuzzy Weighted Averaging (IVITzFWA) operator, interval valued Intuitionistic Trapezoidal Fuzzy Ordered Weighted Averaging (IVITzFOWA) operator and Interval Intuitionistic Trapezoidal Fuzzy Hybrid Averaging (IVITzFHA) operator, are proposed and some desirable properties of these operators are studied, such as Commutativity, Idempotency and Monotonicity. A new distance function and correlation coefficient are proposed for IVITzFNs which will be utilized for ranking the alternatives in MAGDM problems. Finally, numerical illustrations are given to verify the developed approach and to demonstrate its practicality and effectiveness.

Multiple Attribute Group Decision Analysis for Intuitionistic Triangular and Trapezoidal Fuzzy Numbers

Solving Multiple Attribute Group Decision Making (MAGDM) problems has become one of the most important researches in recent days. In situations where the information or the data is of the form of an Intuitionistic Triangular Fuzzy Number (ITrFN) or Intuitionistic Trapezoidal Fuzzy Number (ITzFN), a new distance function is defined for ranking the alternatives in the decision making process. After processing the decision information through a sequence of arithmetic aggregation operators, namely, the Intuitionistic Triangular Fuzzy Weighted Arithmetic Averaging (ITrFWAA), Intuitionistic Triangular Fuzzy Ordered Weighted Averaging (ITrFOWA) operator and the Intuitionistic Triangular Fuzzy Hybrid Aggregation (ITrFHA) operator, the proposed distance function is utilized to rank the best alternative. A model is proposed to solve MAGDM problems using the developed distance formula defined for ITrFNs. Numerical illustration is provided and comparisons are made with some of the existing MAGDM models and ranking procedures.

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.

New Fuzzy Aggregation Operators Based on the Finite Choquet Integral — Application in the MADM Problem

In this paper, new generalizations of the probabilistic averaging operator — Associated Fuzzy Probabilistic Averaging (As-PA and As-FPA) and Immediate Probabilistic Fuzzy Ordered Weighted Averaging (As-IP-OWA and As-IP-FOWA) operators are presented in the environment of fuzzy uncertainty. An uncertainty is presented by associated probabilities of a fuzzy measure. Expert’s evaluations as arguments of the aggregation operators are described by a variable, values of which are compatibility levels on the states of nature defined in positive real or triangular fuzzy numbers (TFNs). Two propositions on the As-FPA operator are proved: (1) The As-FPA operator for the fuzzy measure — capacity of order two coincides with the finite Choquet Averaging (CA) Operator; (2) the As-FPA operator coincides with the FPA operator when a probability measure is used in the role of a fuzzy measure. Analogous propositions for the As-IP-FOWA operator are proved. Some propositions on the connection of the As-FPA and As-IP-FOWA operators are also proved. Information measures — Orness and Divergence for the constructed operators are defined. Some propositions on the connections of these parameters with the corresponding parameters of the finite CA Operator are proved. Two illustrative examples on the applicability of the As-FPA and As-IP-FOWA operators are presented: (1) Several variants of the As-FPA and As-IP-FOWA operators are used for comparison of decision-making results for the problems regarding the fiscal policy of a country; (2) The As-FPA operator is used in the Multi-attribute decision-making (MADM) problem of choosing the best version of the students’ project.

Selecting Multiple Evaluator’s Perception-Oriented Relevant Physical Features of Consumer Goods by Using Fuzzy Data Sensitivity and OWA Operators

AbstractThe assessment of goods quality using experts is costly task in addition to their often unavailability. In this paper, we present a new method for ranking physical features of consumer goods according to their relevancy to multiple evaluators’ perception at different levels and selecting the most important ones for quality characterization. The main contribution of the paper is combining of fuzzy method and ordered weighted averaging (OWA) operators to achieve our aim. The proposed selection method, considered as a Multi-Evaluators and Multi-Criteria Decision Making (ME-MCDM) technique, has been developed using fuzzy sensitivity (FS) criterion for ranking and OWA operator to aggregate the aforementioned ranking lists. Finally, by introducing a smart percolation technique we get automatically the most relevant physical features for a given sensory descriptor. The suggested approach is applied to a selection problem of textile physical features.

A Framework for Triangular Fuzzy Random Multiple-Criteria Decision Making

Most real-world decisions practically occur in extremely complex environments characterized by both fuzziness and randomness. This phenomenon highlights the requirement for new evaluation methods in a fuzzy random environment and new ways to address fuzzy random multiple-criteria decision-making (MCDM) problems. This study reviews fuzzy random variable (FRV) to evaluate fuzzy random decision-making environment. Given the inaccuracy of certain precision formulas proposed in previous studies for the variance of a triangular FRV, this work presents the detailed process of calculating precision variance formulas and discusses several properties of the expectation and variance of triangular FRVs (TFRVs). The united variance of a TFRV vector is also proven to possess non-additive properties. Thus, an ordered weighted averaging (OWA) operator is extended to aggregate fuzzy random data by proposing a triangular fuzzy random OWA operator. Motivated by the idea of mean–variance analysis, an expectation-variance-based method is employed to rank TFRVs. Furthermore, a novel triangular fuzzy random MCDM method is developed, and certain numerical examples are provided to demonstrate the ability of TFRVs to comprehensively assess the performance of a specific alternative. This work also illustrates how the triangular fuzzy random MCDM framework can be extended to any fuzzy random decision-making process.

Features selection of stonewashed denim to a sensory descriptor

A novel approach of physical feature selection is proposed based on the techniques of fuzzy logic and ordered weighted averaging (OWA) operators. The selection is considered as a Multi Expert-Multi Criteria Decision Making (ME-MCDM) problem, since this approach ranks physical features according to their relevancy to multiple experts’ perception. In this study, objective and subjective evaluations of stonewashed denim fabric were made to predict fabric handle in a simple way. The aim of this research was to select the most relevant objective parameters predicting a given subjective one with a minimum number of laboratory measurements. For this purpose, a wide database of treated denim fabrics was prepared by making subjective and objective evaluations. Subjective assessment was evaluated by two different panels. The first one consists of sensorial experts who classify the fabric samples according to their sensitivity to a predefined sensory descriptor. The second one is a panel that masters professional knowledge only in mechanical features of fabrics. The objective measurements were evaluated mainly by means of the Kawabata Evaluating system for fabrics (KES-F), Fabric Assurance by Simple Testing (FAST) system and Universal Surface Tester (UST). The soft computing modelling demonstrate that the fuzzy sensitivity and OWA operators are capable of selecting the most relevant objective parameters notably by introducing a smart percolation method yielding automatically the set of the most objective parameters.

A review on fuzzy multi-criteria decision making land clearing for oil palm plantation

Our review paper research categorize the methods in the method of Fuzzy Multi-Criteria Decision Making (FMCDM) to find the method is widely used in the case of land clearing for plantation. Model FMCDM is used to assess the parameter in multi-criteria-based decision making. The dominant percentage of the result was obtained using Fuzzy Analytic Hierarchy Process (FAHP) method. While the application of other methods for the same problem are Fuzzy Ordered Weighted Averaging (FOWA), Fuzzy Elimination Et Choix Traduisant la Realite or Elimination and Choice Translating Reality (FELECTRE), Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), Fuzzy, Artificial Neural Networks (FANNs) has less. Some the research result also implemented hybrid in FMCDM Method to give some weight in the assessment of decision making. There was also a paper which integrates FMCDM to the GIS method on the land clearing. Therefore, it is concluded that the issue on the land clearing can be done through collaboration of several models of FMCDM, so that it can be developed by involving the decision model using multi-stakeholder model

MAGDM Problems with Correlation Coefficient of Triangular Fuzzy IFS

Correlation coefficient of Intuitionistic Fuzzy Set (IFS), Interval valued IFS, Triangular IFS and Trapezoidal IFS are already present in the literature. This paper proposes the correlation coefficient for Triangular Fuzzy Intuitionistic Fuzzy set (TrFIFS). The method on uncertain Multiple Attribute Group Decision Making (MAGDM) problems based on aggregating intuitionistic fuzzy information is investigated for TrFIFSs. The Triangular Fuzzy Intuitionistic Fuzzy Ordered Weighted Averaging (TrFIFOWA) operator is proposed for TrFIFSs and the Triangular Fuzzy Intuitionistic Fuzzy Ordered Weighted Geometric (TrFIFOWG) operator is utilized for decision making models where expert weights are completely unknown. Based on these operators and the correlation coefficient defined for the TrFIFSs, new decision making models are proposed with numerical illustrations. Some comparisons are also made with existing ranking methods for validity.

Supplier Selection Based on Fuzzy Preference Relation and Ordered Weighted Averaging Operator

Supplier selection has emerged as an irreplaceable element in the supply chain management. Traditional multi-criteria decision-making view to the supplier selection is confronted with some drawbacks such as the aggregation of different criteria. In this paper, fuzzy preference relation is used to express the relative preference of pairwise suppliers, which are based on the linguistic values given by domain experts. Ordered weighted averaging operator is applied to develop two types of coefficients, preference coefficient and aversion coefficient, respectively, to express the preference degree and aversion degree for each supplier. Finally, the best supplier is determined according to the measure of preference coefficient. The effectiveness of the proposed method has been demonstrated by an illustrative example.