Pythagorean fuzzy MULTIMOORA method based on distance measure and score function: its application in multicriteria decision making process

2020 ◽  
Vol 62 (11) ◽  
pp. 4373-4406 ◽  
Author(s):  
Chao Huang ◽  
Mingwei Lin ◽  
Zeshui Xu
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Score Function ◽  
Multicriteria Decision Making ◽  
Decision Making Process ◽  
Multicriteria Decision ◽  
Multimoora Method

A Fuzzy Multicriteria Decision-Making Approach to Crime Linkage

2018 ◽  
Vol 11 (2) ◽  
pp. 31-50 ◽  
Author(s):  
Soumendra Goala ◽  
Palash Dutta
Keyword(s):  
Decision Making ◽  
Linkage Analysis ◽  
Distance Measure ◽  
Multicriteria Decision Making ◽  
High Volume ◽  
Criminal Cases ◽  
Multicriteria Decision ◽  
Fuzzy Distance ◽  
Crime Linkage

This article describes how serial crimes are very interesting for study in the absence of proper and solid evidence. From a high volume of criminal cases of similar types, it is difficult to detect the crimes that were committed by the same offender or not. The process of linking of crimes which were committed by the same offender or offenders is called Crime Linkage Analysis. In this article, a new hesitant fuzzy distance measure has been introduced and a fuzzy multicriteria decision-making approach has been proposed to help Crime Linkage Analysis, which enables us to find to what extent a pair of crime shares a common offender or offenders.

scholarly journals Distance Based Entropy Measure of Interval-Valued Intuitionistic Fuzzy Sets and Its Application in Multicriteria Decision Making

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Tabasam Rashid ◽  
Shahzad Faizi ◽  
Sohail Zafar
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Distance Measure ◽  
Intuitionistic Fuzzy Set ◽  
Multicriteria Decision Making ◽  
Fuzzy Entropy ◽  
Entropy Measure ◽  
Multicriteria Decision ◽  
Intuitionistic Fuzzy ◽  
Interval Valued

Fuzzy entropy means the measurement of fuzziness in a fuzzy set and therefore plays a vital role in solving the fuzzy multicriteria decision making (MCDM) and multicriteria group decision making (MCGDM) problems. In this study, the notion of the measure of distance based entropy for uncertain information in the context of interval-valued intuitionistic fuzzy set (IVIFS) is introduced. The arithmetic and geometric average operators are firstly used to aggregate the interval-valued intuitionistic fuzzy information provided by the decision makers (DMs) or experts corresponding to each alternative, and then the fuzzy entropy of each alternative is calculated based on proposed distance measure. Several numerical examples are solved to demonstrate the application to MCDM and MCGDM problems to show the effectiveness of the proposed approach.

scholarly journals An Extension of the MULTIMOORA Method for Multiple Criteria Group Decision Making Based upon Hesitant Fuzzy Sets

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhi-Hui Li
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Distance Measure ◽  
Group Decision ◽  
Multiple Criteria Decision Making ◽  
Score Function ◽  
Multiple Criteria ◽  
Hesitant Fuzzy Set ◽  
Multiple Objective Optimization ◽  
Multimoora Method

In order to determine the membership of an element to a set owing to ambiguity between a few different values, the hesitant fuzzy set (HFS) has been proposed and widely diffused to deal with vagueness and uncertainty involved in the process of multiple criteria group decision making (MCGDM) problems. In this paper, we develop novel definitions of score function and distance measure for HFSs. Some examples are given to illustrate that the proposed definitions are more reasonable than the traditional ones. Furthermore, our study extends the MULTIMOORA (Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form) method with HFSs. The proposed method thus provides the means for multiple criteria decision making (MCDM) regarding uncertain assessments. Utilization of hesitant fuzzy power aggregation operators also enables facilitating the process of MCGDM. A numerical example of software selection demonstrates the possibilities of application of the proposed method.

scholarly journals A Novel Multicriteria Decision-Making Method Based on Distance, Similarity, and Correlation: DSC TOPSIS

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Semra Erpolat Taşabat
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Pearson Correlation ◽  
Multicriteria Decision Making ◽  
Distance Measures ◽  
Important Place ◽  
Multicriteria Decision ◽  
Development Levels ◽  
The 1960S ◽  
The Right

Decision-making, briefly defined as choosing the best among the possible alternatives within the possibilities and conditions available, is a far more comprehensive process than instant. While in the decision-making process, there are often a lot of criteria as well as alternatives. In this case, methods referred to as Multicriteria Decision-Making (MCDM) are applied. The main purpose of the methods is to facilitate the decision-maker's job, to guide the decision-maker and help him to make the right decisions if there are too many options. In cases where there are many criteria, effective and useful decisions have been taken for granted at the beginning of the 1960s for the first time and supported by day-to-day work. A variety of methods have been developed for this purpose. The basis of some of these methods is based on distance measures. The most known method in the literature based on the concept of distance is, of course, a method called Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In this study, a new MCDM method that uses distance, similarity, and correlation measures has been proposed. This new method is shortly called DSC TOPSIS to include the initials of distance, similarity, and correlation words, respectively, prefix of TOPSIS name. In the method, Euclidean was used as distance measure, cosine was used as similarity measure, and Pearson correlation was used as relation measure. Using the positive ideal and negative-ideal values obtained from these measures, respectively, a common positive ideal value and a common negative-ideal value were obtained. Afterward DSC TOPSIS is discussed in terms of standardization and weighting. The study also proposed three different new ranking indexes from the ranking index used in the traditional TOPSIS method. The proposed method has been tested on the variables showing the development levels of the countries that have a very important place today. The results obtained were compared with the Human Development Index (HDI) value developed by the United Nations.

scholarly journals Comparing Aras and Copras Methods to Evaluate Firms By Using Fundamental Analysis

2020 ◽  
Vol 7 (2) ◽  
pp. 214-230
Author(s):  
Hasan Durmus ◽  
Mehmet Nuri İnel
Keyword(s):  
Decision Making ◽  
Investment Decision ◽  
Investment Decisions ◽  
Multicriteria Decision Making ◽  
Financial Ratios ◽  
Decision Making Process ◽  
Fundamental Analysis ◽  
Management Decisions ◽  
Multicriteria Decision

Decision making process is a though process not only for the management decisions but also for daily decisions. Multicriteria decision making methods were developed to make this process easier. There are many multicriteria decision making methods used in many areas at the present. In this study two of these methods were used, namely ARAS (Additive Ratio Assessment) and COPRAS (Complex Proportional Assessment), for fundamental analysis in investment decisions. Aim of this study is to implement and compare methods on fundamental analysis of firms to make an investment decision. In the study financial ratios of 20 firms from 5 different sectors and 4 different countries, sectoral data and country indicators were used. According to these data, ARAS and COPRAS methods were implemented and although exactly same results were not found, approximately similar results were obtained. The best and the worst companies were same for both methods, even though other rankings differed slightly. Also, same sector selected as best for both methods to invest in.

scholarly journals Some aggregation operators of neutrosophic Z-numbers and their multicriteria decision making method

2020 ◽  
Author(s):  
Shigui Du ◽  
Jun Ye ◽  
Rui Yong ◽  
Fangwei Zhang
Keyword(s):  
Decision Making ◽  
Fuzzy Numbers ◽  
Score Function ◽  
Multicriteria Decision Making ◽  
Neutrosophic Set ◽  
Human Knowledge ◽  
Multicriteria Decision ◽  
Business Partners ◽  
Weighted Arithmetic ◽  
New Framework

Abstract As the generalization of the classical fuzzy number, the concept of Z-number introduced by Zadeh indicates more ability to depict the human knowledge and judgments of both restraint and reliability as an order pair of fuzzy numbers. In indeterminacy and inconsistent environment, a neutrosophic set is described by the truth, falsity, and indeterminacy degrees, but they lack measures related to reliability. To describe the hybrid information of combining the truth, falsity and indeterminacy degrees with their corresponding reliability degrees, this paper first proposes the concept of a neutrosophic Z-number (NZN) set, which is a new framework of neutrosophic values combined with the neutrosophic measures of reliability, as the generalization of the Z-number and the neutrosophic set. Then, we define the operations of neutrosophic Z-numbers (NZNs) and a score function for ranking NZNs. Next, we present NZN weighted arithmetic averaging (NZNWAA) and NZN weighted geometric averaging (NZNWGA) operators to aggregate NZN information and investigate their properties. Regarding the NZNWAA and NZNWGA operators and the score function, a multicriteria decision making (MDM) approach is developed in the NZN environment. Finally, an illustrative example about the selection problem of business partners is given to demonstrate the applicability and effectiveness of the developed MDM approach in NZN setting.

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