Indian banks' productivity ranking via Data Envelopment Analysis and Fuzzy Multi-Attribute Decision-Making hybrid

2008 ◽  
Vol 1 (1) ◽  
pp. 44 ◽  
Author(s):  
C. Pramodh ◽  
V. Ravi ◽  
T. Nagabhushanam
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Multi Attribute Decision Making ◽  
Indian Banks

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.

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

Using Other Multi-Attribute Decision-Making Techniques to Measure Efficiency When Data Envelopment Analysis Fails

2021 ◽  
pp. 247-258
Author(s):  
William P. Fox ◽  
Ethan Buck ◽  
John Morris ◽  
Oliver Stein ◽  
Simon Sun ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Multi Attribute Decision Making

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

scholarly journals Measuring the Productivity of Energy Consumption of Major Industries in China: A DEA-Based Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xishuang Han ◽  
Xiaolong Xue ◽  
Jiaoju Ge ◽  
Hengqin Wu ◽  
Chang Su
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Data Envelopment ◽  
Policy Makers ◽  
Time Periods ◽  
Decision Making Units

Data envelopment analysis can be applied to measure the productivity of multiple input and output decision-making units. In addition, the data envelopment analysis-based Malmquist productivity index can be used as a tool for measuring the productivity change during different time periods. In this paper, we use an input-oriented model to measure the energy consumption productivity change from 1999 to 2008 of fourteen industry sectors in China as decision-making units. The results show that there are only four sectors that experienced effective energy consumption throughout the whole reference period. It also shows that these sectors always lie on the efficiency frontier of energy consumption as benchmarks. The other ten sectors experienced inefficiency in some two-year time periods and the productivity changes were not steady. The data envelopment analysis-based Malmquist productivity index provides a good way to measure the energy consumption and can give China's policy makers the information to promote their strategy of sustainable development.

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

2020 ◽  
Vol 33 (02) ◽  
pp. 431-445
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Confidence Interval ◽  
Data Envelopment ◽  
Data Set ◽  
Interval Model ◽  
Time Period ◽  
Proposed Model ◽  
The Future ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.

scholarly journals A knowledge system proposal with weighted goal programming approach for multi-criteria dea model and an application

2021 ◽  
Vol 12 (2) ◽  
pp. 422-438
Author(s):  
Tugba Polat ◽  
Safak Kiris
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Goal Programming ◽  
Evaluation Process ◽  
Knowledge System ◽  
Programming Approach ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Weighted Goal Programming

In today's competitive environment, enterprises should use their resources correctly; they should continuously improve themselves and work efficiently. It is important to evaluate the performances of the units under the same conditions in enterprises according to each other, to see the current situations and to determine appropriate improvements in necessary points. One of the commonly used approaches to performance evaluation is Data Envelopment Analysis. Many approaches have been developed for the Data Envelopment Analysis model, and Goal programming using in multi-objective decision making solutions approaches is one of them. Goal Programming gives decision-makers the opportunity to evaluate many objectives together in the decision-making process. In this study, classical Data Envelopment Analysis and weighted goal programming approach for multi-criteria data envelopment analysis model was applied in the evaluation process of the projects worked in an automotive supplier industry. A knowledge system has also been proposed in order to evaluate the effectiveness of the projects periodically and to include new projects or conditions into the evaluation.

scholarly journals Analisis Efisiensi Relatif di Unit Pelaksana Teknis Badan Pengawas Obat dan Makanan Tahun 2019

2022 ◽  
Vol 6 (2) ◽  
Author(s):  
Pantri Widyastuti ◽  
Atik Nurwahyuni
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Mixed Method ◽  
Data Envelopment ◽  
Cross Sectional ◽  
Decision Making Unit

Tantangan pengawasan obat dan makanan mengharuskan Unit Pelaksana Teknis (UPT) BPOM bekerja optimal di tengah keterbatasan sumber daya. Analisis efisiensi relatif pada Unit Pelaksana Teknis BPOM tahun 2019 dilakukan bertujuan untuk perbaikan dalam perencanaan, penganggaran, dan kebijakan strategis BPOM dalam upaya peningkatan capaian kinerja pada masing-masing UPT. Perhitungan efisiensi relatif menggunakan metode DEA (Data envelopment Analysis). Penelitian ini menggunakan mixed method dengan desain penelitian cross sectional. Sampel penelitian adalah 31 UPT BPOM yang memenuhi syarat sebagai DMU (Decision Making Unit) dan menggunakan 3 input dan 4 output yang diuji dengan metode DEA. Terdapat 10 informan dalam analisis kualitatif untuk mengetahui strategi dalam pencapaian efisiensi UPT. Hasil dari analisis terdapat 15 UPT yang efisien dan 16 UPT yang tidak efisien. Hasil wawancara diketahui bahwa UPT yang efisien dan yang tidak efisien telah melaksanakan strategi efisiensi internal dengan baik. DEA merupakan analisis efisiensi relatif dengan konsep memaksimalkan rasio output dan input. Penggunaan model VRS (Variabel return to Scale) yang mempertimbangkan proses, diharapkan mengeliminasi kekurangan yang terdapat dalam perhitungan dengan DEA. Perhitungan DEA dilakukan secara mekanik, maka diperlukan pendalaman proses untuk menggali faktor efisiensi yang tidak didapatkan dari perhitungan DEA, terlebih untuk organisasi yang dalam prosesnya melibatkan faktor eksternal yang cukup besar.

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