Decision-making is primarily a process that involves different actors: people, groups of people, institutions and the state. As a discipline, multi-criteria decision-making has a relatively short history. Since 1950s and 1960s, when foundations of modern multi-criteria decision-making methods have been laid, many researches devoted their time to development of new multi-criteria decision-making models and techniques. In the past decades, researches and development in the field have accelerated and seem to continue growing exponentially. Despite the intensive development worldwide, few attempts have been made to systematically present the theoretical bases and developments of multi-criteria decision-making methods. However, the methodological choices and framework for assessment of decisions are still under discussion. The article describes the situation with reviews of MCDM/MADM methods. Furthermore, there is a need for research to study the strengths and weaknesses of different decision-making me...

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State of art surveys of overviews on MCDM/MADM methods

Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry

While Pareto-based multiobjective optimization algorithms continue to show effectiveness for a wide range of practical problems that involve mostly two or three objectives, their limited application for many-objective problems, due to the increasing proportion of nondominated solutions and the lack of sufficient selection pressure, has also been gradually recognized. In this paper, we revive an early developed and computationally expensive strength Pareto-based evolutionary algorithm by introducing an efficient reference direction-based density estimator, a new fitness assignment scheme, and a new environmental selection strategy, for handling both multiobjective and many-objective problems. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental studies demonstrate that the proposed method shows very competitive performance on both multiobjective and many-objective problems considered in this paper. Besides, our extensive investigations and discussions reveal an interesting finding, that is, diversity-first-and-convergence-second selection strategies may have great potential to deal with many-objective optimization.

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A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization

Thank you very much for reading fuzzy multiple attribute decision making methods and applications. As you may know, people have look numerous times for their chosen readings like this fuzzy multiple attribute decision making methods and applications, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious virus inside their desktop computer.

Fuzzy Multiple Attribute Decision Making Methods And Applications

https://cyberleninka.org/article/n/670180.pdf

Fuzzy TOPSIS: A General View

Multi Criteria Decision Making (MCDM) provides strong decision making in domains where selection of best alternative is highly complex. This survey paper reviews the main streams of consideration in multi criteria decision making theory and practice in detail. The main purpose is to identify various applications and the approaches, and to suggest approaches which are most robustly and effectively useable to identify best alternative. This survey work also addresses the problem in fuzzy multi criteria decision making techniques. Multi criteria decision making have been applied in many domains. MCDM method helps to choose the best alternatives where many criteria have come into existence, the best one can be obtained by analyzing the different scope for the criteria, weights for the criteria and the choose the optimum ones using any multi criteria decision making techniques. This survey provides the comprehensive developments of various methods of FMCDM and its applications.

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A Survey on Multi Criteria Decision Making Methods and Its Applications

Decision Tree is the most widely applied supervised classification technique. The learning and classification steps of decision tree induction are simple and fast and it can be applied to any domain. In this research student qualitative data has been taken from educational data mining and the performance analysis of the decision tree algorithm ID3, C4.5 and CART are compared. The comparison result shows that the Gini Index of CART influence information Gain Ratio of ID3 and C4.5. The classification accuracy of CART is higher when compared to ID3 and C4.5. However the difference in classification accuracy between the decision tree algorithms is not considerably higher. The experimental results of decision tree indicate that student's performance also influenced by qualitative factors.

http://mecs-press.org/ijmecs/ijmecs-v5-n5/IJMECS-V5-N5-3.pdf

An Analysis on Performance of Decision Tree Algorithms using Student's Qualitative Data

An information delivery model for banking business

The core of Operation Research is the development of approaches for optional Decision Making. A prominent class of such problem is Multi-Criteria Decision Making MCDM. MCDM is used when different alternatives and different criteria are applied to make better Decision Making. There are several methodologies available in MCDM out which TOPSIS is one of the traditional methods in use. The TOPSIS Method is used to identify solution from a finite set of alternatives based upon simultaneous minimization of distance from a nadir point. In the first step of TOPSIS the vector normalization is performed. In our proposed work different normalization techniques are applied to find the best normalization which suits the TOPSIS Method. It is evaluated based on the performance measures like time and space complexity.

A Comparison Of Various Normalization In Techniques For Order Performance By Similarity To Ideal Solution (topsis)

Business intelligence is an effective technology to take right decisions at right time for the survival of any business. Business intelligence can be applied to all kind decision making and prediction analysis. Business performance can be identified by using bankruptcy prediction. In this research we are developing a business intelligence model to predict the business performance by using bankruptcy prediction as well as we are finding important features to improve the prediction accuracy of bankruptcy model. The proposed BI model applies both Quantitative and Qualitative factors to predict bankruptcy. Quantitative factors are measured from financial variables and Qualitative factors are measured from non-financial variables using data mining techniques. To identify the important features from the quantitative bankruptcy models this research work applies Real Genetic Algorithm. The Real Genetic Algorithm analysis the non linear relation between financial variables in Fulmer bankruptcy model and identifies important features. The experimental result shows that accuracy level of original threshold value α and generated threshold value β is more than 90%.

T. Miranda Lakshmi

Papers

A Survey on Multi Criteria Decision Making Methods and Its Applications

An Analysis on Performance of Decision Tree Algorithms using Student's Qualitative Data

An information delivery model for banking business

A Comparison Of Various Normalization In Techniques For Order Performance By Similarity To Ideal Solution (topsis)

An analysis on business intelligence models to improve business performance