scholarly journals EVALUATION OF ALTERNATIVES APPLYING TOPSIS METHOD IN A FUZZY ENVIRONMENT

2005 ◽  
Vol 11 (4) ◽  
pp. 242-247 ◽  
Author(s):  
Jurgita Antuchevičiene
Keyword(s):  
Real Life ◽  
Fuzzy Topsis ◽  
Multiple Attribute Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Fuzzy Environment ◽  
Attribute Analysis ◽  
Relative Closeness ◽  
Multiple Attribute ◽  
Topsis Technique

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.

scholarly journals TOPSIS Method Based on Correlation Coefficient and Entropy Measure for Linguistic Pythagorean Fuzzy Sets and Its Application to Multiple Attribute Decision Making

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Mingwei Lin ◽  
Chao Huang ◽  
Zeshui Xu
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Fuzzy Topsis ◽  
Multiple Attribute Decision Making ◽  
Entropy Measure ◽  
Security Evaluation ◽  
Topsis Method ◽  
Pythagorean Fuzzy Set ◽  
Multiple Attribute ◽  
Topsis Technique

The linguistic Pythagorean fuzzy set (LPFS) is an important implement for modeling the uncertain and imprecise information. In this paper, a novel TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method is proposed for LPFSs based on correlation coefficient and entropy measure. To this end, the correlation coefficient is proposed for the relationship measurement between LPFSs. Afterwards, two entropy measures are developed to calculate the attribute weight information. Then, a novel linguistic Pythagorean fuzzy TOPSIS (LPF-TOPSIS) method is proposed to solve multiple attribute decision-making problems. Finally, the LPF-TOPSIS method is applied to handle a case concerning the selection of firewall productions, and then, a case concerning the security evaluation of computer systems is given to conduct the comparative analysis between the proposed LPF-TOPSIS method and previous decision-making methods for validating the superiority of the proposed LPF-TOPSIS method.

Model for Software Quality Evaluation with Fuzzy Information

2016 ◽  
Vol 13 (10) ◽  
pp. 7394-7398
Author(s):  
Yi-Ding Zhao ◽  
Zhi-Min Li ◽  
Xi-Guang Zhang
Keyword(s):  
Decision Making ◽  
Relative Entropy ◽  
Software Quality ◽  
Quality Evaluation ◽  
Multiple Attribute Decision Making ◽  
Triangular Fuzzy Number ◽  
Ideal Solution ◽  
Software Quality Evaluation ◽  
Relative Closeness ◽  
Multiple Attribute

To study the problem of multiple attribute decision making in which the decision making information values are triangular fuzzy number, a relative entropy decision making method for software quality evaluation is proposed. Then, according to the concept of the relative entropy, the relative closeness degree is defined to determine the ranking order of all alternatives by calculating the relative entropy to both the fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. At last, a numerical example for software quality evaluation is provided to illustrate the proposed method. The result shows the approach is simple, effective and easy to calculate.

scholarly journals Penerapan Metode Topsis Untuk Pemilihan Vendor Terbaik

2021 ◽  
Vol 11 (2) ◽  
pp. 19-30
Author(s):  
Derman Janner Lubis ◽  
Nur Amalina Anindita
Keyword(s):  
Calculation Method ◽  
Research Method ◽  
Ideal Solution ◽  
Topsis Method ◽  
Decision Matrix ◽  
Relative Closeness ◽  
The Matrix ◽  
Order Preference ◽  
Topsis Technique ◽  
Selection Of

The selection of vendors to work on a project is an activity that must be carried out effectively and precisely so that the project is carried out in accordance with business needs and does not suffer losses. To get the best vendor ranking, you can use the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) calculation method. TOPSIS method is a method that generates rankings by calculating the distance between the best solution and the worst solution. The steps to calculate using TOPSIS are identification of alternatives and their values, create a decision matrix, normalize the matrix, calculate the normalization matrix, look for positive and negative solutions, calculate the distance between positive and negative solutions, and calculate relative closeness and sort preferences. In this study using 8 criteria and 5 alternative vendors. Research method using research and development. This method will produce a prototype. The results of the calculation of TOPSIS obtained vendor c who gets the highest score and vendor b with the lowest rank

scholarly journals SISTEM PENDUKUNG KEPUTUSAN UNTUK MEMILIH USAHA WARALABA MAKANAN MENGGUNAKAN METODE TOPSIS

2013 ◽  
Vol 5 (2) ◽  
pp. 61-68
Author(s):  
Meri Azmi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Multiple Attribute Decision Making ◽  
Ideal Solution ◽  
Shortest Distance ◽  
Multiple Attribute ◽  
Order Preference ◽  
Negative Ideal Solution ◽  
Topsis Technique

Large number of franchises that will be selected as well as indicators of many criteria, it is necessary to build a decision support system that will help decide which franchise to choose. The model used in the decision support system is a Multiple Attribute Decision Making (MADM) and to perform calculations on the case MADM method in finding the best alternative based on the criteria specified use traditional methods TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to perform the calculations. TOPSIS method is chosen as the method is based on the concept that the best alternative was chosen not only has the shortest distance from the positive ideal solution, but also has the longest distance from the negative ideal solution.

scholarly journals Improving the Guidance Learning (LBB) Consumer Satisfaction in Malang using DANP - TOPSIS method

CAUCHY ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 117
Author(s):  
Kwardiniya Andawaningtyas ◽  
Endang Wahyu Handamari ◽  
Corina Karim
Keyword(s):  
Decision Making ◽  
Marketing Strategy ◽  
Consumer Satisfaction ◽  
Multiple Attribute Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Analytical Network Process ◽  
Industrial Services ◽  
Multiple Attribute ◽  
Network Process

Decision analysis of Multiple Attribute Decision Making (MADM) model is used to assess the performance, not only in a rank but also in a plan of marketing strategy as an effort to increase consumers’ satisfaction by combining DEMATEL-based Analytical Network Process (DANP) method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. One of the industrial services in the education nowadays is the services of the Guidance Learning (LBB). This article has 3 alternatives to 6 criteria. The questionnaire was distributed to 80 LBB’ students and 55 LBB’ mentors. The result of the dominant criteria affecting customer satisfaction of LBB in Malang by DANP method is the mentor quality. Meanwhile, the TOPSIS result showed that the LBB of Avicenna Education Malang is the best alternative to the marketing strategy..

TOPSIS in Generalized Intuitionistic Fuzzy Environment

2016 ◽  
pp. 630-642 ◽  
Author(s):  
Amal Kumar Adak ◽  
Debashree Manna ◽  
Monoranjan Bhowmik ◽  
Madhumangal Pal
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Fuzzy Data ◽  
Ideal Solution ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making ◽  
Intuitionistic Fuzzy Data

The aim of this chapter is to investigate the multiple attribute decision making problems to a selected project with generalized intuitionistic fuzzy information in which the information about weights is completely known and the attributes values are taken from the generalized intuitionistic fuzzy environment. Here, we extend the technique for order performance by similarity to ideal solution (TOPSIS) for the generalized intuitionistic fuzzy data. In addition, obtained the concept of possibility degree of generalized intuitionistic fuzzy numbers and used to solve ranking alternative in multi-attribute decision making problems.

A Method to Extend TOPSIS for Fuzzy Multiple Attribute Decision Making

2013 ◽  
Vol 634-638 ◽  
pp. 3936-3939
Author(s):  
Yuan Yuan He ◽  
Zai Wu Gong
Keyword(s):  
Decision Making ◽  
Fuzzy Numbers ◽  
Multiple Attribute Decision Making ◽  
Triangular Fuzzy Numbers ◽  
Fuzzy Information ◽  
Topsis Method ◽  
Fuzzy Distance ◽  
Relative Closeness ◽  
Multiple Attribute ◽  
Two Alternatives

The TOPSIS method is developed for solving the problem of fuzzy multiple attribute decision making, in which the attribute values take the form of triangular fuzzy numbers. A new distance for triangular fuzzy numbers is introduced to measure difference between two alternatives. And we apply the similarity degree derived from the new fuzzy distance to design a model of TOPSIS. Then, we utilize the TOPSIS method to aggregate the fuzzy information corresponding to each alternative, and rank the alternatives according to their relative closeness. Finally, an illustrative example is given to demonstrate the proposed approach practicality and effectiveness.

TOPSIS method for spherical fuzzy MAGDM based on cumulative prospect theory and combined weights and its application to residential location

2022 ◽  
pp. 1-14
Author(s):  
Huiyuan Zhang ◽  
Guiwu Wei ◽  
Cun Wei
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Residential Location ◽  
Fuzzy Topsis ◽  
Cumulative Prospect Theory ◽  
Topsis Method ◽  
Attribute Weights ◽  
Fuzzy Environment ◽  
Multiple Attribute ◽  
Topsis Approach

Nowadays, how to choose a comfortable and relatively satisfactory residence is one of the multiple attribute group decision making (MAGDM) issues which people are paying more and more attention. However, since the inaccuracy and fuzziness of the information are given by decision makers (DMs) in practical decision-making and psychological factors of DMs should be considered in the decision-making process, this paper presents TOPSIS approach based on cumulative prospect theory (CPT) to deal with the MAGDM issues under the spherical fuzzy environment. Furthermore, considering the objective relationship between the attributes, the combined weights are used to get attribute weights in spherical fuzzy sets (SFSs). Finally, an example of residential location is introduced to prove the validity of our proposed approach by comparing with spherical fuzzy TOPSIS(SF-TOPSIS) method and spherical fuzzy WASPAS (SF-WASPAS) method.

An Approach for Solving Fuzzy MADM Problems

1997 ◽  
Vol 05 (04) ◽  
pp. 459-480 ◽  
Author(s):  
Chie-Bein Chen ◽  
Chiu-Chi Wei
Keyword(s):  
Weighted Average ◽  
Practical Method ◽  
Multiple Attribute Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Fuzzy Weighted Average ◽  
Multiple Attribute ◽  
Order Preference ◽  
Fuzzy Madm ◽  
Simple Additive Weighting

An approach using defuzzifying methods is proposed for the fuzzy multiple attribute decision-making (MADM) problems. The computing effectiveness of the proposed defuzzifying methods combined with the simple additive weighting (SAW) method and the technique for order preference by similarity to ideal solution (TOPSIS) method are evaluated based on a comparison to the improved fuzzy weighted average (IFWA) followed by a ranking method. Both SAW and TOPSIS methods are two of classic MADM methods. The purpose of this application is to make the method easier to program and data easier to manipulate. This results in a more practical method for fuzzy decisions. A numerical example and experiment are discussed to demonstrate the implementation of the methods in different input conditions.

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