Critic-entropy based fuzzy decision making models: a systematic analysis

This research article presents a comprehensive analysis of weighting methods used in fuzzy multi attribute decision making (MADM) methods. These methods involve various criteria in order to evaluate alternatives and determining the weights of criteria is a significant problem that arises very often in many MADM problems. In this research paper, CRTITIC and Entropy weighting methods have been used for finding criteria’s weights like in many research works. Using these unsupervised methods of assigning criteria weights, seven fuzzy MADM methods are examined in the context of ranking the best company to invest in. From the results of these methods, ranking order of alternatives is obtained and are analysed for reliability.

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Logarithmic Aggregation Operators of Picture Fuzzy Numbers for Multi-Attribute Decision Making Problems

Mathematics ◽

10.3390/math7070608 ◽

2019 ◽

Vol 7 (7) ◽

pp. 608 ◽

Cited By ~ 10

Author(s):

Saifullah Khan ◽

Saleem Abdullah ◽

Lazim Abdullah ◽

Shahzaib Ashraf

Keyword(s):

Decision Making ◽

Fuzzy Set ◽

Vital Role ◽

Aggregation Operators ◽

Weighted Averaging ◽

Fuzzy Decision Making ◽

Fuzzy Decision ◽

Picture Fuzzy Set ◽

Fuzzy Aggregation ◽

Multi Attribute Decision Making

The objective of this study was to create a logarithmic decision-making approach to deal with uncertainty in the form of a picture fuzzy set. Firstly, we define the logarithmic picture fuzzy number and define the basic operations. As a generalization of the sets, the picture fuzzy set provides a more profitable method to express the uncertainties in the data to deal with decision making problems. Picture fuzzy aggregation operators have a vital role in fuzzy decision-making problems. In this study, we propose a series of logarithmic aggregation operators: logarithmic picture fuzzy weighted averaging/geometric and logarithmic picture fuzzy ordered weighted averaging/geometric aggregation operators and characterized their desirable properties. Finally, a novel algorithm technique was developed to solve multi-attribute decision making (MADM) problems with picture fuzzy information. To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method, and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable.

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Distribution-Based Approaches to Deriving Weights from Dual Hesitant Fuzzy Information

Symmetry ◽

10.3390/sym11010085 ◽

2019 ◽

Vol 11 (1) ◽

pp. 85 ◽

Cited By ~ 4

Author(s):

Zhan Su ◽

Zeshui Xu ◽

Hua Zhao ◽

Shousheng Liu

Keyword(s):

Decision Making ◽

Cognitive Bias ◽

Fuzzy Decision Making ◽

Distribution Characteristics ◽

Fuzzy Decision ◽

Fuzzy Information ◽

Higher Weights ◽

Negative Effect ◽

Main Ideas ◽

Weighting Methods

Modern cognitive psychologists believe that the decision act of cognitive bias on decision results is universal. To reduce their negative effect on dual hesitant fuzzy decision-making, we propose three weighting methods based on distribution characteristics of data. The main ideas are to assign higher weights to the mid arguments considered to be fair and lower weights to the ones on the edges regarded as the biased ones. The means and the variances of the dual hesitant fuzzy elements (DHFEs) are put forward to describe the importance degrees of the arguments. After that, these results are expanded to deal with the hesitant fuzzy information and some examples are given to show their feasibilities and validities.

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Sensitive Analysis on Selection of Piston Material Using MADM Techniques

Strojnícky casopis – Journal of Mechanical Engineering ◽

10.2478/scjme-2019-0042 ◽

2019 ◽

Vol 69 (4) ◽

pp. 45-56

Author(s):

K Lakshmi Chaitanya ◽

Kolla Srinivas

Keyword(s):

Decision Making ◽

Material Selection ◽

Performance Score ◽

Design And Manufacturing ◽

Criteria Weights ◽

New Material ◽

Multi Attribute Decision Making ◽

Selection For ◽

Ranking Order ◽

Selection Of

AbstractDecision making in material selection plays important role in selecting appropriate material based on design and manufacturing attributes. Proposing a new material is always a challenging task so the researchers used Decision making assistance tools. In the Present paper the application of Multi-Attribute Decision Making (MADM) methods are applied to the piston material selection for optimal design process. Comparative study of subjective and objective criteria weights on selected MADM methods are done. Sensitivity analysis is conducted to prove the consistency in performance score ranking order as the criteria weights for each alternative varies.

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Perbandingan Metode Smart dan Maut untuk Pemilihan Karyawan pada Merapi Online Corporation

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021863583 ◽

2021 ◽

Vol 8 (6) ◽

pp. 1205

Author(s):

Musri Iskandar Nasution ◽

Abdul Fadlil ◽

Sunardi Sunardi

Keyword(s):

Decision Making ◽

Employee Selection ◽

Decision Makers ◽

Criteria Weights ◽

Multi Attribute Decision Making ◽

Waterfall Model ◽

Ranking Order ◽

Alternative Ranking ◽

And Function ◽

Employee Data

Penelitian ini merancang sistem untuk menentukan pemilihan karyawan terbaik menggunakan Sistem Pendukung Keputusan (SPK). Perhitungan sistem menggunakan metode SMART dan MAUT. SMART merupakan metode pengambilan keputusan multiatribut yang setiap alternatif terdiri dari sekumpulan atribut dan setiap atribut mempunyai nilai-nilai. Sedangkan MAUT didasarkan pada konsep dimana pembuat keputusan dapat menghitung utilitas dari setiap alternatif menggunakan fungsi MAUT dan dapat memilih alternatif dengan utilitas tertinggi. Metode SMART digunakan karena perhitungannya lebih sederhana dan memungkinkan penambahan serta pengurangan alternatif tanpa mempengaruhi perhitungan pembobotan mengingat jumlah karyawan bisa berkurang dan bertambah secara tidak teratur. Sedangkan metode MAUT digunakan karena memunculkan hasil urutan peringkat dimana akan muncul hasil nilai terbesar sampai nilai terkecil sehingga dapat diketahui karyawan dengan terbaik dengan nilai tertinggi. Sehingga dapat mengambil keputusan dengan efektif atas persoalan yang kompleks dengan menyederhanakan dan mempercepat proses pengambilan keputusan. Metode penelitian yang digunakan adalah metode pengembangan sistem model waterfall, metodologi ini terdapat tahapan-tahapan kegiatan yang harus dilakukan dalam merancang suatu sistem. Perhitungan menggunakan 30 sampel data karyawan dan empat kriteria penilaian. Empat kriteria tersebut adalah presensi dengan bobot 40, masa kerja dengan bobot 30, ijin dengan bobot 20, dan disiplin dengan bobot 10. Data karyawan yang digunakan adalah karyawan yang sama dalam kedua metode serta mempunyai data penilaian yang sama. Hasil perhitungan menggunakan SMART dan MAUT menunjukkan bahwa keduanya dapat diimplementasikan dan berfungsi dengan baik untuk menentukan karyawan terbaik. Dengan menggunakan data alternatif, nilai alternatif, dan bobot kriteria yang sama diperoleh hasil bahwa metode SMART memberikan hasil yang lebih baik dengan 22 peringkat, sedangkan metode MAUT menghasilkan 18 peringkat. Semakin banyak jumlah peringkat yang muncul maka semakin baik karena mampu meminimalisir nilai preferensi yang sama, sehingga perankingan alternatif dapat dilakukan dengan baik. AbstractThis study designed a system to determine the best employee selection using a Decision Support System (SPK). System calculations using the SMART and MAUT methods. SMART is a multi-attribute decision making method in which each alternative consists of a set of attributes and each attribute has values. Whereas MAUT is based on the concept where decision makers can calculate the utility of each alternative using the MAUT function and can choose alternatives with the highest utility. The SMART method is used because the calculation is simpler and allows the addition and subtraction of alternatives without affecting the weighting calculation given the number of employees can be reduced and increased irregularly. While the MAUT method is used because it raises the ranking order results in which the largest value will appear until the smallest value so that it can be known by the employee with the highest value. So that they can make decisions effectively on complex issues by simplifying and accelerating the decision making process. The research method used is the method of developing the system waterfall model, this methodology there are stages of activities that must be carried out in designing a system. The calculation uses 30 employee data samples and four assessment criteria. The four criteria are presence with a weight of 40, tenure with a weight of 30, permission with a weight of 20, and discipline with a weight of 10. Employee data used are the same employees in both methods and have the same assessment data. The results of calculations using SMART and MAUT indicate that both can be implemented and function properly to determine the best employees. By using alternative data, alternative values, and the same criteria weights, the results obtained that the SMART method gives better results with 22 ratings, while the MAUT method yields 18 ratings. The more number of ratings that appear, the better because it is able to minimize the same preference value, so that alternative ranking can be done well.

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A Universal Routing Algorithm Based on Intuitionistic Fuzzy Multi-Attribute Decision-Making in Opportunistic Social Networks

Symmetry ◽

10.3390/sym13040664 ◽

2021 ◽

Vol 13 (4) ◽

pp. 664

Author(s):

Yao Yu ◽

Jiong Yu ◽

Zhigang Chen ◽

Jia Wu ◽

Yeqing Yan

Keyword(s):

Decision Making ◽

Success Rate ◽

Routing Algorithm ◽

Relay Node ◽

Fuzzy Decision Making ◽

Fuzzy Decision ◽

Network Resources ◽

Relay Nodes ◽

Intuitionistic Fuzzy ◽

Multi Attribute Decision Making

With the vigorous development of big data and the 5G era, in the process of communication, the number of information that needs to be forwarded is increasing. The traditional end-to-end communication mode has long been unable to meet the communication needs of modern people. Therefore, it is particularly important to improve the success rate of information forwarding under limited network resources. One method to improve the success rate of information forwarding in opportunistic social networks is to select appropriate relay nodes so as to reduce the number of hops and save network resources. However, the existing routing algorithms only consider how to select a more suitable relay node, but do not exclude untrusted nodes before choosing a suitable relay node. To select a more suitable relay node under the premise of saving network resources, a routing algorithm based on intuitionistic fuzzy decision-making model is proposed. By analyzing the real social scene, the algorithm innovatively proposes two universal measurement indexes of node attributes and quantifies the support degree and opposition degree of node social attributes to help node forward by constructing intuitionistic fuzzy decision-making matrix. The relay nodes are determined more accurately by using the multi-attribute decision-making method. Simulation results show that, in the best case, the forwarding success rate of IFMD algorithm is 0.93, and the average end-to-end delay, network load, and energy consumption are the lowest compared with Epidemic algorithm, Spray and Wait algorithm, NSFRE algorithm, and FCNS algorithm.

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