An Improved Fuzzy TOPSIS Method with a New Ranking Index

2021 ◽  
pp. 1-27
Author(s):  
S. A. Sadabadi ◽  
A. Hadi-Vencheh ◽  
A. Jamshidi ◽  
M. Jalali
Keyword(s):  
Real Life ◽  
Similarity Index ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Triangular Fuzzy Numbers ◽  
Linguistic Terms ◽  
Cost Criterion ◽  
Negative Ideal Solution ◽  
Ranking Index

Owing to vague concepts frequently represented in decision data, the crisp values are inadequate to model real-life situations. In this paper, the rating of each alternative and the weight of each criterion is described by linguistic terms which can be expressed in triangular fuzzy numbers. Next, we focus on fuzzy TOPSIS (FTOPSIS) method. We show that, however, the conventional FTOPSIS is interesting, but it suffers from some flaws. The shortcomings of classical FTOPSIS are shown and some solutions are given. Further, a new similarity index is proposed and then is illustrated using numerical examples. By treating the separations of an alternative from the fuzzy positive ideal solution (FPIS) and the fuzzy negative ideal solution (FNIS) as “cost” criterion and “benefit” criterion, respectively, we reduce the original fuzzy multiple criteria decision making (FMCDM) problem to a new one with two criteria. Illustrative examples are given to show the advantages of the proposed approach.

A New Index for TOPSIS based on Relative Distance to Best and Worst Points

2020 ◽  
Vol 19 (03) ◽  
pp. 695-719
Author(s):  
S. A. Sadabadi ◽  
A. Hadi-Vencheh ◽  
A. Jamshidi ◽  
M. Jalali
Keyword(s):  
Decision Making ◽  
Similarity Index ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Relative Distance ◽  
Ideal Solution ◽  
Topsis Method ◽  
Numerical Examples ◽  
Shortest Distance ◽  
Negative Ideal Solution

The technique for order performance by similarity to ideal solution (TOPSIS) is one of the most well-known methods in multiple criteria decision making (MCDM) problems. The classical TOPSIS method employs a similarity index to rank alternatives. However, the chosen alternative sometimes does not have the shortest distance to the positive ideal solution (PIS) and remotest distance from the negative ideal solution (NIS), simultaneously. Besides, in some cases, TOPSIS cannot assign a unique rank to alternatives. The purpose of this paper is to propose a new similarity TOPSIS index based on the relative distance to the best and worst points. In the proposed method, by treating the separations of an alternative from the PIS and the NIS as negative criterion and positive criterion, respectively, we reduce the original MCDM problem to a new one with two criteria. The proposed index, based on different weights, in optimistic, pessimistic, and apathetic cases, easily determines the score of each alternative. Finally, we illustrate the proposed index using four numerical examples. The results are compared with those published in the literature.

Designing and Evaluating Safety Accident Emergency Management for Construction Project under Fuzzy Environment

2011 ◽  
Vol 243-249 ◽  
pp. 6307-6311
Author(s):  
Cheng Hua Li ◽  
Yun Xiu Sai ◽  
Hui Mao
Keyword(s):  
Emergency Management ◽  
Construction Industry ◽  
Fuzzy Numbers ◽  
Construction Project ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Triangular Fuzzy Numbers ◽  
Fuzzy Environment ◽  
Safety Accident ◽  
Negative Ideal Solution

Recently, safety accident emergency management of construction project is an important issue not only for investors and governments but also for the companies that are in construction industry. Designing and evaluating safety accident emergency management is also crucial for the same sector’s development. Proposed method is based on fuzzy TOPSIS, which combined TOPSIS with triangular fuzzy numbers both in the rating of alternative and the weight of criterion. The closeness coefficient under fuzzy environment is calculated by using the concept of fuzzy positive-ideal solution and fuzzy negative-ideal solution. Then the rankings of the projects are determined according to their results.

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.

Intuitionistic Fuzzy Distance-Based Intuitionistic Fuzzy TOPSIS Method and Application to MADM

2017 ◽  
pp. 72-96
Author(s):  
Jiangxia Nan ◽  
Ting Wang ◽  
Jingjing An
Keyword(s):  
Distance Measure ◽  
Fuzzy Topsis ◽  
Distance Measures ◽  
Ideal Solution ◽  
Intuitionistic Fuzzy Numbers ◽  
Metric Properties ◽  
Fuzzy Distance ◽  
Intuitionistic Fuzzy ◽  
Multi Attribute Decision Making ◽  
Negative Ideal Solution

In this paper, an intuitionistic fuzzy (IF) distance measure between two triangular intuitionistic fuzzy numbers (TIFNs) is developed. The metric properties of the proposed IF distance measures are also studied. Then, based on the IF distance, an extended TOPSIS is developed to solve multi-attribute decision making (MADM) problems with the ratings of alternatives on attributes of TIFNs. In this methodology, the IF distances between each alternative and the TIFN positive ideal-solution are calculated as well as the TIFN negative ideal-solution. Then the relative closeness degrees obtained of each alternative to the TIFN positive ideal solution are TIFNs. Based on the ranking methods of TIFNs the alternatives are ranked. A numerical example is examined to the validity and practicability of the method proposed in this paper.

scholarly journals Project Delivery System Decision Making using Pythagorean Fuzzy TOPSIS

2019 ◽  
Vol 30 (4) ◽  
pp. 461-471
Author(s):  
Limin Su ◽  
Huimin Li ◽  
Yongchao Cao ◽  
Lelin Lv
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Fuzzy Topsis ◽  
Delivery Systems ◽  
Project Delivery ◽  
Ideal Solution ◽  
Negative Ideal Solution ◽  
Selection Of

The selection of project delivery systems is a complex decision-making process, which is also a critical task for owners. The complexity problem arises from the uncertainty of decision making environment and construction project itself. Pythagorean fuzzy sets (PFS), as an extension from intuitionistic fuzzy sets (IFSs) to deal with uncertainty information, has attracted more scholars’ attention in the decision making area. In this paper, we develop three similarity measures (i.e., 1-type PFSs similarity measure, 2-type PFSs weighted similarity measure, 3-type PFSs weighted similarity measure), and investigate their properties. Then an improved TOPSIS decision making framework is further established with PFSs information, in which the proposed similarity measures are employed to measure the similarity degree between each alternative and negative ideal solution and positive ideal solution. Finally, a case study of the selection of project delivery systems is presented to proof the performance of the proposed decision making method.

scholarly journals Fuzzy Similarity in Multicriteria Decision-Making Problem Applied to Supplier Evaluation and Selection in Supply Chain Management

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Pasi Luukka
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supplier Selection ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Similarity Criteria ◽  
Fuzzy Decision Making ◽  
Ideal Solution ◽  
Supply Chain System ◽  
Fuzzy Similarity

It is proposed to use fuzzy similarity in fuzzy decision-making approach to deal with the supplier selection problem in supply chain system. According to the concept of fuzzy TOPSIS earlier methods use closeness coefficient which is defined to determine the ranking order of all suppliers by calculating the distances to both fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. In this paper we propose a new method by doing the ranking using similarity. New proposed method can do ranking with less computations than original fuzzy TOPSIS. We also propose three different cases for selection of FPIS and FNIS and compare closeness coefficient criteria and fuzzy similarity criteria. Numerical example is used to demonstrate the process. Results show that the proposed model is well suited for multiple criteria decision-making for supplier selection. In this paper we also show that the evaluation of the supplier using traditional fuzzy TOPSIS depends highly on FPIS and FNIS, and one needs to select suitable fuzzy ideal solution to get reasonable evaluation.

A Study and Estimation of Different Distance Measures in Generalized Fuzzy TOPSIS to Improve Ranking Order

2019 ◽  
pp. 207-236
Author(s):  
Martin Aruldoss ◽  
Miranda Lakshmi Travis ◽  
Prasanna Venkatesan Venkatasamy
Keyword(s):  
Fuzzy Topsis ◽  
Distance Measures ◽  
Ideal Solution ◽  
Distance Method ◽  
Distance Methods ◽  
Relative Closeness ◽  
Bit Vector ◽  
Ranking Order ◽  
Negative Ideal Solution ◽  
Target Alternative

Multi criteria decision making (MCDM) is used to solve multiple conflicting criteria. There are different methods available in MCDM out of which TOPSIS is a well- known method to solve precise and imprecise information. In this chapter, triangular fuzzy TOPSIS is considered which has different steps like normalization, weight, finding of positive ideal solution (PIS) and negative ideal solution (NIS), distance between PIS and NIS, calculating relative closeness coefficient (RCC) value and ranking the alternatives. Out of these different steps a distance method is studied. The distance measures are basically used to find the distance between the target alternative and the best and the least alternatives. The most commonly used distance method is Euclidean distance. Many other distance methods are available such as Manhattan, Bit-vector, Hamming, Chebyshev distance, etc. To obtain the appropriate distance, these methods are evaluated. The proposed approach is applied in banking domain to find the suitable user for multi criteria reporting (MCR).

scholarly journals An extension of TOPSIS based on linguistic terms in triangular intuitionistic fuzzy structure

2021 ◽  
pp. 409-424
Author(s):  
Muhammad Saeed ◽  
Asad Mehmood ◽  
Amna Anwar
Keyword(s):  
Euclidean Distance ◽  
Ideal Solution ◽  
Intuitionistic Fuzzy Number ◽  
Linguistic Terms ◽  
Modern Approach ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Triangular Intuitionistic Fuzzy Number ◽  
Negative Ideal Solution ◽  
Fuzzy Framework

Chen [24] introduced the extension of TOPSIS in the fuzzy structure, while this article stretches the modern approach of TOPSIS to the intuitionistic fuzzy framework. Linguistic terms are used in this study to evaluate the weight of each criterion and the rating of alternatives in the context of a triangular intuitionistic fuzzy number. A new intuitionistic fuzzy positive ideal solution (IFPIS) and intuitionistic fuzzy negative ideal solution (IFNIS) are proposed in this model of extended TOPSIS. Euclidean distance is introduced between two triangular intuitionistic fuzzy numbers to calculate separation between each alternative to both (IFPIS) and (IFNIS). The proposed model’s mechanism is presented with the help of an algorithm, and then it is applied to the personal selection problem. Finally, a comparative study is given between this model and other TOPSIS techniques.

An Intuitionistic Fuzzy Multi-Criteria Decision-Making Method Based on an Exponential-Related Function

2017 ◽  
Vol 6 (4) ◽  
pp. 33-46 ◽  
Author(s):  
Daniel Aikhuele ◽  
Faiz Turan
Keyword(s):  
Decision Making ◽  
Real Life ◽  
Multiple Criteria Decision Making ◽  
Score Function ◽  
Pairwise Comparison ◽  
Ideal Solution ◽  
Related Function ◽  
Intuitionistic Fuzzy ◽  
Relative Closeness ◽  
Priority Weights

Intuitionistic fuzzy multiple criteria decision making (MCDM) method which is based on an exponential-related function, adopted in the Technique for order preference by similarity to ideal solution (TOPSIS) has been proposed in this study. The exponential-related function which is used for comparing intuitionistic-fuzzy-sets (IFS), and as a replacement for the traditional exponential score function which is only effective for determining priority weights that involve pairwise-comparison, has been applied, for computing the separation measure from the fuzzy positive and negative ideal solution to determine the relative closeness-coefficients of alternatives. The main advantage of this method includes (1) its ability to account for Decision-makers (DMs) attitudinal-character in the decision-making process as-well-as to represent the aggregated effect of the positive/negative evaluations in the performance ratings of the alternatives based on the IFS-data and (2) The simplicity of the method both in its concept and computational procedures. To demonstrate the feasibility of the method, it has been applied for the evaluation of some hypothetical design-related problems and for a real-life case study.

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