The New Method for Ranking Grouped Credit Customer Based on DEA Method

The New Method for Credit Customer Selecting by Integration of A2 and Data Envelopment Analysis (A2_DEA)

This paper develops a decision support tool using an A2 method and data envelopment analysis (DEA) approach (A2-DEA). This new method is applied for the bank credit customer selection problem and credit scoring as a pilot survey at Export Development Bank of Iran. The proposed method has led to fewer calculations, faster and more accurate decision making, less complexity, and ability to ana...

Maximum appreciative cross-efficiency in DEA: A new ranking method

Ranking decision making units (DMUs) is one of the most important applications of data envelopment analysis (DEA). In this paper, we exploit the power of individual appreciativeness in developing a methodology that combines crossevaluation, preference voting and ordered weighted averaging (OWA). We show that each stage of the proposed methodology enhances discrimination among DMUs while offerin...

A New Method for Ranking Distribution Companies with Several Scenarios Data by Using DEA/MADM

In Data Envelopment Analysis, uncertain data are the inseparable part of real models. Natural models usually deal with uncertain and probable data. Many researchers prioritize these kinds of data. For instance, they study fuzzy data, interval data, probabilistic models etc. In this article, we proposed a method in which the decision making units are uncertain in their inputs and outputs. In the...

FUZZY RISK ANALYSIS BASED ON A NEW METHOD FOR RANKING GENERALIZED FUZZY NUMBERS

Fuzzy risk analysis, as a powerful tool to address uncertain information, can provide an appropriate method for risk analysis. However, the previous fuzzy risk analysis methods still have some weaknesses. To overcome the weaknesses of existing fuzzy risk analysis methods, a novel method for ranking generalized fuzzy numbers is proposed for addressing fuzzy risk analysis problems. In the propose...

A new parametric method for ranking fuzzy numbers based on positive and negative ideal solutions

A Method for Determining the Importance of Customer Demand Based on Rough Set and DEA

Affected by customers’ lack of experiences and personal preferences, the importance of customer demand as 0 by only using Rough Set method frequently occurs. Existing methods could not determine this importance of indicators, so it is usually deleted. A new method combining Rough Set and Data Envelopment Analysis (DEA) to determine importance of customer demand in Quality Function Deployment (Q...