Ranking decision making units based on the multi-directional efficiency measure

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

Download Full-text

Teaching and Learning Process of Decision-Making Units in Talented Young Players From U-10 to U-14

Frontiers in Psychology ◽

10.3389/fpsyg.2020.00600 ◽

2020 ◽

Vol 11 ◽

Author(s):

Juan Carlos Pastor-Vicedo ◽

Alejandro Prieto-Ayuso ◽

Onofre Ricardo Contreras-Jordán ◽

Filipe Manuel Clemente ◽

Pantelis Theo Nikolaidis ◽

...

Keyword(s):

Decision Making ◽

Learning Process ◽

Teaching And Learning ◽

Decision Making Units

Download Full-text

TECHNICAL EFFICIENCY OF PUBLIC DECISION MAKING UNITS

Policy Studies Journal ◽

10.1111/j.1541-0072.1983.tb00275.x ◽

1983 ◽

Vol 12 (2) ◽

pp. 337-346 ◽

Cited By ~ 11

Author(s):

Roger B. Parks

Keyword(s):

Decision Making ◽

Technical Efficiency ◽

Decision Making Units ◽

Public Decision ◽

Public Decision Making

Download Full-text

Developing an approach for decision making units integration using multi objective particle swarm optimization

International Journal of Engineering & Technology ◽

10.14419/ijet.v4i1.3775 ◽

2014 ◽

Vol 4 (1) ◽

pp. 48 ◽

Cited By ~ 1

Author(s):

Abdorrahman Haeri ◽

Kamran Rezaie ◽

Seyed Morteza Hatefi

Keyword(s):

Decision Making ◽

Particle Swarm Optimization ◽

Performance Measures ◽

Particle Swarm ◽

Final Decision ◽

Swarm Optimization ◽

Multi Objective ◽

Decision Making Unit ◽

The Difference ◽

Decision Making Units

In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively. Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.

Download Full-text

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

Mathematical Problems in Engineering ◽

10.1155/2014/121804 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 6

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.

Download Full-text

A Novel Enhanced Russell Graph Efficiency Measure in Network DEA

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622022500018 ◽

2021 ◽

pp. 1-31

Author(s):

Robabeh Eslami ◽

Mohammad Khoveyni

Keyword(s):

Efficiency Score ◽

Network System ◽

Network Structures ◽

Efficiency Measure ◽

Network Dea ◽

Economic Implications ◽

Multi Stage ◽

Proposed Model ◽

Decision Making Units ◽

High Computational Complexity

Hitherto, the presented models for measuring the efficiency score of multi-stage decision-making units (DMUs) either are nonlinear or require to specify the weights for combining their divisional efficiencies. The nonlinearity leads to high computational complexity for these models, especially when used for problems with enormous dimensions, and also assigning various weights to the divisional efficiencies causes to obtain different efficiency scores for the multi-stage network system. To tackle these problems, this study contributes to network DEA by introducing a novel enhanced Russell graph (ERG) efficiency measure for evaluating the general two-stage series network structures. Then, the proposed model is extended into the general multi-stage series network structures. This study also describes the managerial and economic implications of measuring the efficiency score of the multi-stage DMUs and provides two numerical and empirical examples for illustrating the use of our proposed model.

Download Full-text

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

Nexo Revista Científica ◽

10.5377/nexo.v33i02.10782 ◽

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.

Download Full-text