A novel dynamic credit risk evaluation method using data envelopment analysis with common weights and combination of multi-attribute decision-making methods

2021 ◽  
Vol 129 ◽  
pp. 105223
Author(s):  
Jalil Heidary Dahooie ◽  
Seyed Hossein Razavi Hajiagha ◽  
Shima Farazmehr ◽  
Edmundas Kazimieras Zavadskas ◽  
Jurgita Antucheviciene
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Evaluation Method ◽  
Data Envelopment ◽  
Common Weights ◽  
Multi Attribute Decision Making ◽  
Using Data

Road safety risk evaluation and target setting using data envelopment analysis and its extensions

2012 ◽  
Vol 48 ◽  
pp. 430-441 ◽  
Author(s):  
Yongjun Shen ◽  
Elke Hermans ◽  
Tom Brijs ◽  
Geert Wets ◽  
Koen Vanhoof
Keyword(s):  
Data Envelopment Analysis ◽  
Road Safety ◽  
Risk Evaluation ◽  
Data Envelopment ◽  
Target Setting ◽  
Safety Risk ◽  
Using Data

scholarly journals New Approach in Fixed Resource Allocation and Target Setting Using Data Envelopment Analysis with Common Set of Weights

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Marzieh Ghasemi ◽  
Mohammad Reza Mozaffari ◽  
Farhad Hosseinzadeh Lotfi ◽  
Mohsen Rostamy malkhalifeh ◽  
Mohammad Hasan Behzadi
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Target Setting ◽  
New Approach ◽  
Common Set Of Weights ◽  
Programming Techniques ◽  
Using Data ◽  
Decision Making Units

One of the mathematical programming techniques is data envelopment analysis (DEA), which is used for evaluating the efficiency of a set of similar decision-making units (DMUs). Fixed resource allocation and target setting with the help of DEA is a subject that has gained much attention from researchers. A new model was proposed by determining a common set of weights (CSW). All DMUs were involved with the aim of achieving higher efficiency in every DMU after the procedure. The minimum resources and targets allocated to each DMU were commensurate to the efficiency of that DMU and the share of DMU in the input resources and the output productions. To examine the proposed method, other methods in the DEA literature were examined as well, and then, the efficiency of the method was demonstrated through a numerical example.

Perfect Partners of Mathematical Modeling with Technology in Risk Assessment

2016 ◽  
pp. 61-90
Author(s):  
William P. Fox
Keyword(s):  
Mathematical Modeling ◽  
Risk Assessment ◽  
Game Theory ◽  
Decision Making ◽  
Sensitivity Analysis ◽  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Discrete Dynamical Systems ◽  
Data Envelopment ◽  
Multi Attribute Decision Making

This chapter discusses the use of mathematical modeling with technology in risk assessment in the broad area of operations research. The authors provide modeling as a process and illustrate suggested steps in the process. This chapter reviews some of the main modeling texts and provide a brief discussion of their processes. Many illustrative examples are provided to show the breadth of mathematical modeling. These examples cover such topics as discrete dynamical systems, game theory, multi-attribute decision making, data envelopment analysis with linear programming, and integer programming. The authors discuss the important of sensitivity analysis, as applicable. Several scenarios are used as illustrative examples of the process.

scholarly journals Cross-Efficiency Evaluation Method with Compete-Cooperate Matrix

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Qiang Hou ◽  
Xue Zhou
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Evaluation Method ◽  
Peer Assessment ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Self Assessment ◽  
Dea Method ◽  
Decision Making Units

Cross-efficiency evaluation method is an effective and widespread adopted data envelopment analysis (DEA) method with self-assessment and peer-assessment to evaluate and rank decision making units (DMUs). Extant aggressive, benevolent, and neutral cross-efficiency methods are used to evaluate DMUs with competitive, cooperative, and nontendentious relationships, respectively. In this paper, a symmetric (nonsymmetric) compete-cooperate matrix is introduced into aggressive and benevolent cross-efficiency methods and compete-cooperate cross-efficiency method is proposed to evaluate DMUs with diverse (relative) relationships. Deviation maximization method is applied to determine the final weights of cross-evaluation to enhance the differentiation ability of cross-efficiency evaluation method. Numerical demonstration is provided to illustrate the reasonability and practicability of the proposed method.

scholarly journals Dimension-Specific Efficiency Measurement Using Data Envelopment Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hongjun Zhang ◽  
Youliang Zhang ◽  
Rui Zhang
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
The Other ◽  
Data Envelopment ◽  
Input Output ◽  
Single Dimension ◽  
Measuring Efficiency ◽  
Using Data ◽  
The City ◽  
Decision Making Units

Data envelopment analysis (DEA) is a powerful tool for evaluating and improving the performance of a set of decision-making units (DMUs). Empirically, there are usually many DMUs exhibiting “efficient” status in multi-input multioutput situations. However, it is not appropriate to assert that all efficient DMUs have equivalent performances. Actually, a DMU can be evaluated to be efficient as long as it performs best in a single dimension. This paper argues that an efficient DMU of a particular input-output proportion has its own specialty and may also perform poorly in some dimensions. Two DEA-based approaches are proposed to measure the dimension-specific efficiency of DMUs. One is measuring efficiency in multiplier-form by further processing the original multiplier DEA model. The other is calculating efficiency in envelopment-form by comparing with an ideal DMU. The proposed approaches are applied to 26 supermarkets in the city of Nanjing, China, which have provided new insights on efficiency for the managers.

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