Fuzzy Multiple Attribute Decision Making Methods Research Articles

An intelligent decision methodology for triangular Pythagorean fuzzy MADM and applications to college English teaching quality evaluation

Abstract The development of contemporary college English teaching methods and practice should respect the subjective will of college students; fully tap their wisdom and potential; constantly innovate college English teaching methods, teaching practice activities, classroom teaching, and teaching evaluation models; create a good teaching atmosphere; and establish the concept of developmental evaluation. Through the innovation of teaching methods, college students’ actual language communication ability is cultivated, the teaching objectives and teaching methods of college English reading are innovated, and interactive, body language, role play, situational teaching, communicative teaching, and other teaching methods are used. The College English teaching quality evaluation is a classical multiple attribute decision making (MADM). In this article, the triangular Pythagorean fuzzy sets (TPFSs) are introduced, and the MADM problem is investigated under TPFSs. Based on the traditional dual generalized weighted Bonferroni mean operator and power average operator, the triangular Pythagorean fuzzy dual generalized power Bonferroni mean (TPFDGPBM) operator is proposed. Accordingly, the TPFDGPBM operator is employed to construct the triangular Pythagorean fuzzy MADM method. Finally, a numerical example for College English teaching quality evaluation is constructed to verify the TPFDGPBM technique.

A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation

. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets.

Research on the Correlation between Industry 4.0 and Sustainable Development in the Machine Tool industry – Application of QFD and Fuzzy MADM methods

<p>Starting in 2025, Taiwan will introduce a requirement that listed companies with a paid-in capital of less than 2 billion NTD need to prepare a sustainability report. Most of the actual income scale of Taiwan’s machine tool industry falls within this range. In response to the requirement, the machine tool industry must reduce its impact on the environment and society and strike a balance while increasing economic returns. This is undoubtedly a major challenge for the machine tool industry, which is one of the most internationally competitive industries in Taiwan. If sustainable development (SD) can be successfully implemented, it will set a benchmark for Taiwan’s traditional industries.</p> <p>The machine tool industry has continued to improve its industrial competitiveness in recent years, especially after Germany proposed the concept of Industry 4.0 in 2013, which accelerated the pace of industrial upgrading and transformation. Studies have shown that the adoption of Industry 4.0 technology can effectively enhance the sustainable performance of enterprises. Additionally, in the literature on SD, it has been confirmed that there is a significant positive relationship between corporate social responsibility and profitability.</p> <p>This study will propose a reasonable and feasible model framework to demonstrate the relationship between SD factors and Industry 4.0 technical indicators, aiming to identify the key Industry 4.0 technologies to improve SD. This study uses the Quality Function Deployment (QFD) method as the basic structure and adopts the Fuzzy Delphi Method (FDM) of the Multiple Attribute Decision Making (MADM), the improved Fuzzy Extended Analytic Hierarchy Process (FEAHP), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for prioritizing performance index factors.</p>

Intuitionistic fuzzy multiple attribute decision making method based on a parametric piecewise score function

The score function and aggregation operator provide the theoretical basis for intuitionistic fuzzy multiple attribute decision making (MADM), where the role of the score function is to compare and rank the intuitionistic fuzzy numbers (IFNs), and the role of the aggregation operator is to integrate the evaluation information. In view of that, this paper proposes a new score function and aggregation operator, and a new intuitionistic fuzzy MADM method. It first constructs a parametric piecewise score function and discusses the influence of hesitancy degree and the parameter λ on it. Moreover, some properties of the constructed score function are discussed, including the monotonicity, maximum, minimum, and range. Then, it introduces a intuitionistic fuzzy modified Einstein aggregation (IFMEWA) operator and proves monotonicity, idempotency and boundedness of IFMEWA operator. Finally, combined with the constructed score function and IFMEWA operator, it proposes a new intuitionistic fuzzy MADM method.

Developing a Decision Support System of Land Location for Apartment Investment Using Fuzzy MADM Yager Model

Determining the location of land for apartment investment is usually done manually by relying on intuition, experience, and some considerations from the apartment developer. For this reason, the determination of the location of the land must go through the right analysis and calculation so that the best location can be determined from various criteria. Fuzzy Multiple Attribute Decision Making (FMADM) method Yager model is a decision support system method that can be used in this system. This FMADM method will compare alternative locations with predetermined criteria including land prices, land area, road access, land conditions, and the environment. The final result of this process is the selection of total values to obtain alternative locations, so that the best results from three alternative locations are obtained, namely Banguntapan with the largest total value of 0.600, Seturan with values of 0.085, and Kulon Progo with a value of 0.292. So the location of Banguntapan can be used as a recommendation for the developer in making a decision to determine the location of the land for apartment investment

Hazard Level of Vehicle Smoke by Fuzzy Multiple Attribute Decision Making with Simple Additive Weighting Method

Clean air is a necessity for human. Human needs clean and healthy air without being contaminated by vehicle smoke that can endanger human health. vehicle smoke contains harmful substances for human including carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx) and lead metal (Pb). This can make air quality worse and can cause human be suffered of diseases including ARI (Acute Respiratory Infection), shortness of breath, sore eyes, coughing, brain damage and mental disability in children. Decision Support System (DSS) is a system to support Managerial decision makers in semi-structured decision situations and in this research author made a study of the DSS model to determine the level of danger of vehicle smoke, this study uses the Fuzzy Multiple Attribute Decision Making method (FMADM) where this method is a way to find optimal alternative from a number of alternatives with certain criteria, from very low things that was from 0-0.25, medium from 0.3-0.50, to the most dangerous thing that was from 0.8-1. Hopefully with the existence of this research later it can be useful for the community, especially for public health.

Six Sigma project selection using fuzzy multiple attribute decision-making method

In recent year, Six Sigma has been known as a framework for quality improvement. An appropriate Six Sigma project can increase benefits and customer satisfaction and reduce cost of poor quality and unnecessary waste. This paper presents a comprehensive fuzzy framework for combining objective ratings obtained from quantitative data and subjective ratings obtained from qualitative analysis to select adequate Six Sigma projects. The framework systematically constructs the objectives of Six Sigma project selection to support the business goals and strategies. These objectives can be used to develop tailor-made attributes and a consistent evaluation standard. A fuzzy multiple attribute decision-making method (FMADM) is adopted to resolve the ambiguities involved in assessing Six Sigma projects and aggregating the linguistic evaluation descriptions and weights. The proposed framework can facilitate the complex Six Sigma project selection process, echo company requirements, and consolidate efforts to enhance group decision-making process. An actual example demonstrates the feasibility and of effectiveness applying the proposed framework.

Extension of the TODIM Method to Intuitionistic Linguistic Multiple Attribute Decision Making

Practical decision situations are becoming increasingly complicated. It is common for a person to select or rank alternatives with respect to multiple attributes, and the TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) method, which is one of the first multiple attribute decision making (MADM) methods based on prospect theory, has received more attention due to its great performance in considering the bounded rationality of decision makers (DMs). However, the classical TODIM method can only handle the MADM problems with crisp numbers. In this paper, considering that intuitionistic linguistic variables are convenient to describe uncertain or imprecise information, we propose the intuitionistic linguistic TODIM (IL-TODIM) method and intuitionistic uncertain linguistic TODIM (IUL-TODIM) method to solve uncertain MADM problems with IL and IUL variables, respectively. Additionally, a novel distance measure for IUL numbers is developed, based on which we can obtain the corresponding dominance degree of one alternative over another. Finally, examples are provided to show the validity of the proposed methods, and we also conduct a comparison of the results between the IL-TODIM method and the existing intuitionistic fuzzy MADM methods to illustrate the effectiveness of our proposed methods.

New Interval-Valued Intuitionistic Fuzzy Behavioral MADM Method and Its Application in the Selection of Photovoltaic Cells

As one of the emerging renewable resources, the use of photovoltaic cells has become a promise for offering clean and plentiful energy. The selection of a best photovoltaic cell for a promoter plays a significant role in aspect of maximizing income, minimizing costs and conferring high maturity and reliability, which is a typical multiple attribute decision making (MADM) problem. Although many prominent MADM techniques have been developed, most of them are usually to select the optimal alternative under the hypothesis that the decision maker or expert is completely rational and the decision data are represented by crisp values. However, in the selecting processes of photovoltaic cells the decision maker is usually bounded rational and the ratings of alternatives are usually imprecise and vague. To address these kinds of complex and common issues, in this paper we develop a new interval-valued intuitionistic fuzzy behavioral MADM method. We employ interval-valued intuitionistic fuzzy numbers (IVIFNs) to express the imprecise ratings of alternatives; and we construct LINMAP-based nonlinear programming models to identify the reference points under IVIFNs contexts, which avoid the subjective randomness of selecting the reference points. Finally we develop a prospect theory-based ranking method to identify the optimal alternative, which takes fully into account the decision maker’s behavioral characteristics such as reference dependence, diminishing sensitivity and loss aversion in the decision making process.

An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment

Interval type-2 fuzzy set (IT2FS) offers interesting avenue to handle high order information and uncertainty in decision support system (DSS) when dealing with both extrinsic and intrinsic aspects of uncertainty. Recently, multiple attribute decision making (MADM) problems with interval type-2 fuzzy information have received increasing attentions both from researchers and practitioners. As a result, a number of interval type-2 fuzzy MADM methods have been developed. In this paper, we extend the VIKOR (VlseKriterijumska Optimizacijia I Kompromisno Resenje, in Serbian) method based on the prospect theory to accommodate interval type-2 fuzzy circumstances. First, we propose a new distance measure for IT2FS, which is comes as a sound alternative when being compared with the existing interval type-2 fuzzy distance measures. Then, a decision model integrating VIKOR method and prospect theory is proposed. A case study concerning a high-tech risk evaluation is provided to illustrate the applicability of the proposed method. In addition, a comparative analysis with interval type-2 fuzzy TOPSIS method is also presented.

Selection of the best well control system by using fuzzy multiple-attribute decision-making methods

There are numerous difficulties involved in drilling operations of an oil well, one of the most important of them being well control. Well control systems are applied when we have irruption of liquids or unwanted intrusion of the reservoir's liquid (oil, gas or brine) into the well, during drilling when the pressure of well fluid column is less than formation pressure, and the permeability of the reservoir has a value that is able to pass the liquid through. For this purpose, a variety of methods including Driller, wait and weight, and the concurrent methods were used to control the well at different drilling sites. In this study, we investigate the optimum method for well control using a fussy method based on many parameters, including technical factors (mud weight, drilling rate, blockage of pipes, sensitivity to drilling network changes, etc.) and security factors (existence of effervescent mud, drilling circuit control, etc.), and cost of selection, which is one of the most important decisions that are made under critical conditions such as irruption. Till now, these methods were selected based on the experience of field personnel in drilling sites. The technical criteria and standards were influenced by experience, so the soft computerizing system (fuzzy method) was used. Thus, both these criteria and standards would be of greater importance and indicate whether the optimum numerical method is the same one that is expressed by human experience. The concurrent method was selected as the best for well control, using the fuzzy method at the end of the evaluation, while field personnel experience suggests the Driller method.

Supplier selection in supply chain management by using fuzzy multiple-attribute decision-making method

Supplier selection plays a key role in supply chain management that has received increasing emphasis at every place and has become an important strategic issue for its benefit. Researchers regarded the supplier selection process as a central issue in management decision. In fact, it can be viewed as a fuzzy multiple-attribute decision-making FMADM problem. However, in the past, few articles utilized FMADM methods to deal with the problem of supplier selection. In this paper, we identify empirically the quantitative and qualitative attributes and their corresponding importance weightings for selecting suppliers. Furthermore, a FMADM approach is employed to choose an optimum supplier from all possible supplier candidates.

A comparison study of fuzzy MADM methods in nuclear safeguards evaluation

Nuclear safeguards evaluation (NSE) is to verify that a State is living up to its international undertakings not to use nuclear programs for nuclear weapons purposes. The main issue in NSE is on the aggregation of expert evaluations for numerous indicators to make a final decision about the State's nuclear activity. Fuzzy multiple attribute decision making (FMADM) methods are capable of dealing with such an issue. In this study, we propose a new FMADM methodology to solve the NSE problem. To this end, we investigate the applicability of four basic FMADM methods, namely a simple additive weighting method, a TOPSIS method, a linguistic method, and a non-compensatory method, to the NSE issue. As a result of the assessment of the basic methods, we propose a new FMADM methodology based on a new aggregation operator in which a cumulative belief structure is used to represent the expert evaluations. The basic methods and the proposed method as well are applied to an example from the literature for illustration purposes.

Evaluation of alternatives applying TOPSIS method in a fuzzy environment

Abstract The paper analyses the problem of multiple attribute decision‐making (MADM) under fuzzy environment. In some cases the crisp value is inadequate to model real‐life situations. For this reason some fuzzy MADM methods have been developed. The extended TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) to fuzzy environment is presented in the current paper. Weights and ratings of each criterion are described in triangular fuzzy numbers. The relative closeness to the ideal solution of each alternative is calculated applying different approaches that were presented in different scientific papers. A computational experiment is presented to compare the results of a multiple attribute analysis that uses three modifications of fuzzy TOPSIS method in a particular situation.

EVALUATION OF ALTERNATIVES APPLYING TOPSIS METHOD IN A FUZZY ENVIRONMENT

The paper analyses the problem of multiple attribute decision‐making (MADM) under fuzzy environment. In some cases the crisp value is inadequate to model real‐life situations. For this reason some fuzzy MADM methods have been developed. The extended TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) to fuzzy environment is presented in the current paper. Weights and ratings of each criterion are described in triangular fuzzy numbers. The relative closeness to the ideal solution of each alternative is calculated applying different approaches that were presented in different scientific papers. A computational experiment is presented to compare the results of a multiple attribute analysis that uses three modifications of fuzzy TOPSIS method in a particular situation.

Fuzzy Multiple Attribute Decision Making for Evaluating Aggregate Risk in Green Manufacturing

Industrial risk and the diversification of risk types both increase with industrial development. Many uncertain factors and high risk are inherent in the implementation of new green manufacturing methods. Because of the shortage of successful examples and complete and certain knowledge, decision-making methods using probabilities to represent risk, which need many examples, cannot be used to evaluate risk in the implementation of green manufacturing projects. Therefore, a fuzzy multiple attribute decision-making (FMADM) method was developed with a three-level hierarchical decision-making model to evaluate the aggregate risk for green manufacturing projects. A case study shows that the hierarchical decision-making model of the aggregate risk and the FMADM method effectively reflect the characteristics of the risk in green manufacturing projects.