Fuzzy Set Approach Research Articles

Top Management Team Composition and New Ventures’ IPO Performance: A Fuzzy Set Approach

Prior research on the relationship between top management team (TMT) composition and firm performance has not been able to fully capture the joint influence of simultanously present TMT characteris...

A production inventory supply chain model with partial backordering and disruption under triangular linguistic dense fuzzy lock set approach

This paper focuses on a three-level distribution process in a supply chain (SC) modeling where the raw materials are received batchwise with imperfect quality. Defective batches are rejected instantly under “all or none” policy. Allowing partial backlogging and random disruption in supply, we develop an expected average cost function of the production inventory SC model first. Then, considering the several cost components of the model as linguistic triangular dense fuzzy lock set, the cost function itself has been fuzzified according to the needs of the problem defined at case study. Utilizing the proper application (growth) of key vectors, the objective function has been solved under crisp, general fuzzy, dense fuzzy, dense fuzzy lock of single and double keys environment, respectively. For managerial importance, numerical results and graphical illustrations are made to justify the novelty.

Fuzzy approach in modeling static and fatigue strength of composite materials and structures

For composite materials and structures static strength, fatigue damage and durability demonstrate a scatter factor of results larger than for isotropic materials. To characterize it the fuzzy set approach is proposed. Two different mechanical descriptions of fatigue life are used in order to describe the uncertainty and randomness of parameters characterizing the fatigue damage and finally the fatigue durability. The theoretical predictions representing the lower and upper bounds of a fatigue life are compared with experimental data. In general, the present analysis shows that the fuzzy set description allows us to take into account much more parameters than classical deterministic or statistical methods. The scatter of the results is significantly dependent on the form of the used approximations of fatigue lives.

A fuzzy similarity-based rough set approach for attribute selection in set-valued information systems

Databases obtained from different search engines, market data, patients’ symptoms and behaviours, etc., are some common examples of set-valued data, in which a set of values are correlated with a single entity. In real-world data deluge, various irrelevant attributes lower the ability of experts both in speed and in predictive accuracy due to high dimension and insignificant information, respectively. Attribute selection is the concept of selecting those attributes that ideally are necessary as well as sufficient to better describe the target knowledge. Rough set-based approaches can handle uncertainty available in the real-valued information systems after the discretization process. In this paper, we introduce a novel approach for attribute selection in set-valued information system based on tolerance rough set theory. The fuzzy tolerance relation between two objects using a similarity threshold is defined. We find reducts based on the degree of dependency method for selecting best subsets of attributes in order to obtain higher knowledge from the information system. Analogous results of rough set theory are established in case of the proposed method for validation. Moreover, we present a greedy algorithm along with some illustrative examples to clearly demonstrate our approach without checking for each pair of attributes in set-valued decision systems. Examples for calculating reduct of an incomplete information system are also given by using the proposed approach. Comparisons are performed between the proposed approach and fuzzy rough-assisted attribute selection on a real benchmark dataset as well as with three existing approaches for attribute selection on six real benchmark datasets to show the supremacy of proposed work.

Operational risk assessment of railway train based on type-2 intuitionistic fuzzy set and dynamic VIKOR approach

This paper is devoted to applying fuzzy risk-ranking analysis for the railway train operational risk assessment. Specifically, a new risk-ranking approach of dynamic VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) combined with Type-2 intuitionistic fuzzy set (Type-2 IFS) theory is proposed under different stages, which can be better employed to rank the risk of system components considering the calculation uncertainty, expert preference, and information indeterminacy based on the hybrid index system of quantitative and qualitative information. A specific case is performed to validate the proposed approach. Based on the results, a comparative analysis between the proposed approach and the static risk assessment is presented, which shows the effectiveness and feasibility of the proposed approach.

An EPQ model for three-layer supply chain with partial backordering and disruption: Triangular dense fuzzy lock set approach

This article deals with three-echelon supply chain (SC) network involving flow of raw materials with imperfect quality, the manufacturer and multiple retailers under the effect of learning experiences in fuzzy decision-making process. Existing literature explores the SC model under full backordering and disruption. Thus, in this study we first develop a production-inventory control problem accompanied with partial backlogging and random disruptions. Any batch received from the supplier is inspected by the manufacturer and if any of them are found to be flawed then all the goods in the inspected batch are rejected. However, we present a case study for problem definition and to comprehend the model into practical applicability. To minimize the aggregate cost of the SC we have utilized the Triangular dense fuzzy lock set for controlling the cost vector of the proposed objective function of the model. Utilizing new defuzzification method and applying the proper keys, chosen by the decision maker, it is possible to minimize the average system cost exclusively. Finally, graphical illustrations and sensitivity analysis are made to justify the model.

Weight selection strategies for ordered weighted average based fuzzy rough sets

Fuzzy rough set theory models both vagueness and indiscernibility in data, which makes it a very useful tool for application to various machine learning tasks. In this paper, we focus on one of its robust generalisations, namely ordered weighted average based fuzzy rough sets. This model uses a weighted approach in the definition of the fuzzy rough operators. Although its efficacy and competitiveness with state-of-the-art machine learning approaches has been well established in several studies, its main drawback is the difficulty in choosing an appropriate weighting scheme. Several options exist and an adequate choice can greatly enhance the suitability of the ordered weighted average based fuzzy rough operators. In this work, we develop a clear strategy for the weighting scheme selection based upon the underlying characteristics of the data. The advantages of the approach are presented in a detailed experimental study focusing. Rather than to propose a classifier, our aim is to present a strategy to select a suitable weighting scheme for ordered weighted average based fuzzy rough sets in general. Our weighting scheme selection process allows users to take full advantage of the versatility offered by this model and performance improvements over the traditional fuzzy rough set approaches.

ENERGY ENTERPRISE RISKS ANALYSIS USING FUZZY LOGIC METHODS

In Russia's contemporary conditions, enterprises fail to make the full use of the state-of-the-art financial risks management toolkit. Both diagnosing methods and criterial norms within the mechanism of managing the energy enterprises are not adapted to the current crisis conditions of economic management, which renders it essential to develop the financial management tools that will take into account the industry-related specific nature and anti-crisis constituent of the organizations. In the work, the opportunity of applying the fuzzy logic method for analyzing the financial risks of energy enterprises in Russia is explored. For this purpose, the analysis and assessment of financial situation have been performed for a modeled energy enterprise and bankruptcy risk of the enterprise has been analyzed using the fuzzy logic method. It is demonstrated that the process of managing financial risks at the enterprise using the fuzzy sets approach is relevant in the contemporary conditions of energy enterprises functioning in Russia. There has been found the problem of having to improve the integrated risk management of energy enterprises.Keywords: Energy enterprise; risk management; financial risks; bankruptcy risk; business activity indicator; concept of economic value added (EVA); fuzzy logic methodsJEL Classifications: O13, P28, P48DOI: https://doi.org/10.32479/ijeep.7957

A new membership function approach to uncertain functions

A fuzzy set approach to uncertain functions is in the focus of this paper. The uncertainty in functions entering a model, a differential equation, for instance, is represented by α-dependent sets of crisp functions, that is, α-cuts determined by an integral membership functional that is defined by means of a continuous auxiliary function whose design depends on the information the analyst has about the uncertainty. A scalar quantity of interest evaluating the model output is assumed and its membership function is inferred from the Zadeh extension principle by solving α-dependent worst- and best-case scenario problems. Two types of auxiliary functions are considered and their algorithmic advantages and disadvantages are discussed.

Fuzzy set approach to hyperideal theory of po-ternary semihypergroups

The aim of this paper is to apply fuzzy sets in hyperstructure theory. We initiate fuzzy hyperideals over po-ternary semihypergroups and explore some related properties. We introduce the concept of po-fuzzy point in po-ternary semihypergroups. Moreover, we give a number of characterizations for regular po-ternary semihypergroups by using the concept of fuzzy hyperideals.

법정협회의 유형에 관한 연구 : 퍼지셋 이상형 분석을 적용하여

법정협회의 유형에 관한 연구 : 퍼지셋 이상형 분석을 적용하여

Searching for the Various Effects of Subprograms in Official Development Assistance on Human Development across 15 Asian Countries: Panel Regression and Fuzzy Set Approaches

Previous studies are limited in identifying the effectiveness of each country to seek sectoral support rather than integrated aid. However, it is hypothesized that sector-specific aid by Official Development Assistance (ODA) may be more effective than total aid. This study aims to identify the determinants of economic growth and the living standard levels in 15 Asian countries, focusing particularly on the effects of Official Development Assistance (ODA). In order to explore this research question, we have used two indexes: (1) the annual ODA grants to Korea, with aid type as the key independent variable; and (2) the human development index (HDI), to measure dependent variables from 2006 to 2016, across the 15 Asian countries. Special attention has been paid to understand which is more significant on human development, the effects of each type of aid program individually, and the whole amount of ODA assistance. We have constructed a panel model and a fuzzy set ideal type model to account in the data for qualitative attributes by recipient countries. We have found that the economic and social impacts of ODA on the basis of panel data are significant and that our instrumental variable (IV) method illustrates a statistically significant impact of the total ODA on the HDI of the recipient countries. By separating the total amount of ODA into economic and social sectors, we have found that specific programs of public service, medical care, and welfare are more likely to directly affect HDI. While the total amount of ODA still has a positive impact on HDI, education, health, and the public service field, aids also have significant effects on HDI. Although the effect of sector-specific aid in the water and sanitation field grant-aid is not significant in panel regression results, our fuzzy set method shows that, even if education aid is low, HDI is estimated to increase if the level of health and public service aid is high. Our empirical findings suggest that (1) sector-specific aid may be more effective than total aid with ineffective sub-aid programs and that (2) an optimally specific combination of various sub-programs in ODA may exist for each developing country.

Is the Trier Social Stress Test used similarly across studies: A systematic review of the methodology

This article gives a critical review of the development and applications of fuzzy set and fuzzy logic approach to geographical analysis, in general, and geographical information, in particular. Because of the intrinsic fuzziness, it is essential to develop a formal methodology for the analysis of spatial structures and processes. Within the fuzzy set framework, fundamental concepts such as distance, direction, and connection can be characterized by our natural language with formal representations that respect intrinsic imprecision. Concept of a region, fuzziness of boundaries, and graduation of phenomena over space can be appropriately captured and analyzed. Spatial economic behavior is also governed by a fuzzy preference-indifference structure on which decisions are made. Due to imprecision and to accommodate for flexibility, objectives and constraints may be fuzzily specified and the planning process can be treated as a fuzzy optimization problem. Since binary logic–based geographical information system (GIS) is not adequate to handle information, topological relationships, and operations involving fuzziness, fuzzy logic is a general method to construct GIS on which spatial reasoning and decisions are made. Fuzzy set and fuzzy logic, together with other paradigms such as probability and rough set, will provide a powerful and flexible framework for uncertainty analysis, particularly in the big data era.

Big data governance, dynamic capability and decision-making effectiveness: Fuzzy sets approach

Big data governance, dynamic capability and decision-making effectiveness: Fuzzy sets approach

A fuzzy-based approach to improve the human pick-to-light efficiency incorporated with robots behavior in an intelligent distribution center

For the logistics engineering industry developing rapidly, artificial intelligence (AI) robots like Kiva Systems have been widespread applied to the warehouse operations management in the distribution center. Among operations, many state-of-the-art parts-to-picker robots are introduced to help human workers to improve their pick-to-light operation efficiency. However, at present the human decision approaches that take physical and mental aspects into consideration in an AI-oriented order-picking warehouse are rare. Therefore, how to design an order picking decision for the pick-to-light strategy interacting with the robot has become one important factor in the warehouse. The aim of this paper is to propose an efficient procedure based on the fuzzy set theory to solve the pick-to-light planning problem with parts-to-picker robots in the AI-oriented computer aided picking system (CAPS). Apart from traditional picking strategies that pursue time-reduction performance for orders, fuzzy set approaches provide proper mechanism for reflecting the human physical and mental loading to determine the picking sequences when robots arrive. A pick-to-light strategy that analyzes mutual behavior and operation relations of human and robots is presented here. The objective aimed to achieve is to reduce picking flow time under acceptable human physical and mental loading for the order fulfillment. An empirical example is to illustrate the solution procedure and validate the proposed method that is capable of improving productivity and ergonomic quality simultaneously for pick-to-light operations.

\((\varepsilon,\delta)\)-Characteristic Fuzzy Sets Approach to the Ideal Theory of \(BCK/BCI\)-Algebras

The notion of \((\varepsilon,\delta)\)-characteristic fuzzy sets is introduced. Given an ideal \(F\) of a \(BCK/BCI\)-algebra \(X\), conditions for the \((\varepsilon,\delta)\)-characteristic fuzzy set in \(X\) to be an \((\in, \in \! \vee \, {q})\)-fuzzy ideal, an \((\in, {q})\)-fuzzy ideal, an \((\in, \in \! \wedge \, {q})\)-fuzzy ideal, a \((q,q)\)-fuzzy ideal, a \((q, \in)\)-fuzzy ideal, a \((q, \in \! \vee \, {q})\)-fuzzy ideal and a \((q, \in \! \wedge \, {q})\)-fuzzy ideal are provided. Using the notions of \((\alpha, \beta)\)-fuzzy ideal \(\mu_F^{(\varepsilon,\delta)}\), conditions for the \(F\) to be an ideal of \(X\) are investigated where \((\alpha, \beta)\) is one of \((\in, \in \! \vee \, {q})\), \((\in, \in \! \wedge \, {q})\), \((\in,q)\), \((q,\in \! \vee\, {q})\), \((q,\in \! \wedge \,{q})\), \((q, \in)\) and \((q, {q})\).

Fuzzy Measures of Multidimensional Poverty in the Mediterranean Area: A Focus on Financial Dimension

The main scope of the paper is to adopt a fuzzy sets approach for the measurement of multidimensional poverty over a period of eight years, from 2007 to 2015, which takes into account the effect of the 2008 economic and financial crisis. In particular, the focus is on the financial dimension of poverty, and its effects on citizens in the EU Mediterranean Area. The empirical analysis, based on the European Union—Statistics on Income and Living Conditions survey (EU-SILC), covers eight Mediterranean Countries.

Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection

In this paper, rough, fuzzy and soft set approaches have been integrated to develop a multi-class classification model to assist the farmers in taking decision on crop cultivation for a given agriculture land. The model is divided into three major sections, namely weight calculation of variables, conversion of continuous data to fuzzified values and classification rule generation. Dominance-based rough set approach is used for the calculation of relative weights of variables. Fuzzy proximity relation is applied to convert the continuous data into fuzzified values. Bijective soft set approach is used to generate classification rules for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi. The developed model has been tested with agriculture dataset which showed 92% accuracy for the validation dataset and proved to be confident and robust for agriculture development. Further, the performance of the proposed model is compared with three popular classifiers such as naive Bayes, support vector machine and J48. The obtained experimental results showed high predictive performance, and the potential of the proposed model is compared with the other classifiers.

Mining latent information in PTSD psychometrics with fuzziness for effective diagnoses

The options of traditional self-report rating-scale, like the PTSD Checklist Civilian (PCL-C) scale, have no clear boundaries which might cause considerable biases and low effectiveness. This research aimed to explore the feasibility of using fuzzy set in the data processing to promote the screening effectiveness of PCL-C in real-life practical settings. The sensitivity, specificity, Youden’s index etc., of PCL-C at different cutoff lines (38, 44 and 50 respectively) were analyzed and compared with those of fuzzy set approach processing. In practice, no matter the cutoff line of the PCL-C was set at 50, 44 or 38, the PCL-C showed good specificity, but failed to exhibit good sensitivity and screening effectiveness. The highest sensitivity was at 65.22%, with Youden’s index being 0.64. After fuzzy processing, the fuzzy-PCL-C’s sensitivity increased to 91.30%, Youden’s index rose to 0.91, having seen marked augmentation. In conclusion, this study indicates that fuzzy set can be used in the data processing of psychiatric scales which have no clear definition standard of the options to improve the effectiveness of the scales.

Variable precision multigranulation rough fuzzy set approach to multiple attribute group decision-making based on λ-similarity relation

Abstract This study proposes a fuzzy multigranulation rough set approach to the problem of multiple attribute group decision-making with uncertainty. Based on the classical Pawlak rough set theory, we define the λ - similarity ( 0 ⩽ λ ≤ 1 ) relation classes over the universe of discourse by introducing a distance measure to all alternatives with respect to attribute set. Subsequently, we present the α ( 0.5 α ≤ 1 ) rough approximation of a crisp decision-making object and a fuzzy decision-making object under the framework of multigranulation rough set theory, respectively. That is, we establish the variable precision multigranulation rough set model and variable precision multigranulation rough fuzzy set model based on λ - similarity relation, respectively. Meanwhile, we discuss the interrelationship between the proposed multigranulation rough fuzzy set model and the existing generalized rough set models. After that, we construct a new approach to multiple attribute group decision-making problems based on variable precision multigranulation rough fuzzy set theory. The decision-making procedure and the methodology as well as the algorithm of the proposed method are given and a detailed comparison of the traditional methods to multiple attribute group decision-making problems illustrates the advantages and limitations. Finally, an example of handling multiple criteria group decision-making problem of evaluation of emergency plans for unconventional emergency events illustrates this approach. The main contribution of this paper is twofold. One is to provide a new way to construct multigranulation rough set model with the fuzzy environment. Another is to try making a new way to handle multiple criteria group decision-making problems based on generalized rough set theory and methodologies.