Ranking of DMUs Based on Efficiency Intervals

2011 ◽  
Vol 15 (1) ◽  
pp. 63-67
Author(s):  
Tomoe Entani ◽  
Keyword(s):  
Order Relation ◽  
Interval Data ◽  
Efficiency Measurement ◽  
Decision Makers ◽  
Data Envelopment ◽  
Partial Order Relation ◽  
Output Data ◽  
Decision Making Unit ◽  
The Difference ◽  
Analysis Interval

The efficiency of the Interval Data Envelopment Analysis (Interval DEA), we proposed, obtains its bounds from optimistic and pessimistic viewpoints. Intervals represent the uncertainty of given input-output data and the intuitive evaluation of decision makers. The partial order relation that intervals give elements may be complex, especially when elements are numerous. The efficiency measurement we propose combining optimistic and pessimistic efficiency in Interval DEA is comparable because both represent the difference of the analyzed Decision Making Unit (DMU) from the most efficient one. The efficiency measurement is defined as their minimum and determined mainly by pessimistic efficiency. Optimistic efficiency is considered if it is inadequate compared to pessimistic efficiency. Pessimistic efficiency based evaluation resembles natural evaluation and DMUs are arranged linearly.

Comparing Data Envelopment Analysis and Human Decision Making Unit Rankings: A Survey Approach

2014 ◽  
Vol 29 (2) ◽  
Author(s):  
Stanko Dimitrov
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Decision Makers ◽  
Data Envelopment ◽  
Rank Ordering ◽  
Decision Making Unit ◽  
Human Decision ◽  
Survey Approach ◽  
Decision Making Units ◽  
Better Than

AbstractIn this paper we compare the ordinal rankings generated through Data Envelopment Analysis (DEA) methods to ordinal rankings generated by human decision makers. Through eliciting the total rank ordering for approximately 100 individuals on all of the four different datasets of Decision Making Units (DMUs), we compare the rankings generated by individuals to those generated by ten DEA methods. We observe that depending on the characteristics of the dataset one of the DEA methods performs better than the others in matching human decision makers.

A New MIP Approach on the Least Distance Problem in DEA

2020 ◽  
Vol 37 (06) ◽  
pp. 2050027
Author(s):  
Xu Wang ◽  
Kuan Lu ◽  
Jianming Shi ◽  
Takashi Hasuike
Keyword(s):  
State Of The Art ◽  
Efficient Frontier ◽  
Decision Makers ◽  
Mixed Integer ◽  
Data Envelopment ◽  
Output Level ◽  
Decision Making Unit ◽  
Dea Models ◽  
Distance Problem ◽  
User Friendly

In this paper, we deal with the least distance problem (LDP) in Data Envelopment Analysis (DEA), which is to find a closest efficient target over the whole efficient frontier. To this end, we define the efficient frontier by a linear complementarity system and propose a mixed integer programming (MIP) approach to solve the LDP. Our proposed MIP approach: (1) can solve the LDP based on [Formula: see text]-norm ([Formula: see text]) by using a state-of-the-art solver and obtain the closest efficient target over the whole efficient frontier instead of a subset of it; (2) can be applied for computing the least distance DEA models satisfying the monotonicity; (3) is more user-friendly, because it allows a decision maker to improve the efficiency of a decision making unit (DMU) by setting the affordable input/output level under his/her circumstance. Thus, the efficient target provided by our approach may be more appropriate from the perspective of the decision makers of DMUs.

Interval enhanced Russell measure with undesirable outputs based on data envelopment analysis: An efficiency measurement of industry in China

2021 ◽  
Vol 40 (1) ◽  
pp. 103-115
Author(s):  
Xu Wang ◽  
Ying-Ming Wang
Keyword(s):  
Industrial Development ◽  
Efficiency Measurement ◽  
Decision Makers ◽  
Data Envelopment ◽  
Fuzzy Data ◽  
Undesirable Outputs ◽  
Undesirable Output ◽  
The World ◽  
Chinese Industry ◽  
Complex Production System

China has attracted the attention of the world owing its significant economic achievements, which are supported significantly by its booming industry. However, the issues of energy and pollutants have severely challenged the sustainability of the industry. The efficiency measurement is the premise intended to realize sustainability within the Chinese industry. Because the industry is a complex production system, there exists uncertainties and fuzziness regarding its inputs and outputs. This study proposes the application of an interval to describe these fuzzy data and employ the Enhanced Russell Measure to assess the performance of the Chinese industry, accounting for undesirable output such as pollution. In addition, for the ranking between interval efficiencies, a novel ranking approach based on the holistic acceptability of a possibility degree is proposed. The proposed method provides advice and guidance for decision makers to make appropriate and effective policies to balance industrial development and environmental protection in spite of uncertain and fuzzy data.

Generalized Fuzzy Sample DEA Model and its Application in the Evaluation of Projects

2011 ◽  
Vol 63-64 ◽  
pp. 407-411
Author(s):  
Ren Mu ◽  
Zhan Xin Ma ◽  
Wei Cui ◽  
Yun Morigen Wu
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Input Output ◽  
Output Data ◽  
Dea Model ◽  
Decision Making Unit ◽  
Fuzzy Input ◽  
Fuzzy Dea

Evaluating the performance of activities or organizations by traditional data envelopment analysis model requires crisp input/output data. However, in real-world problems inputs and outputs are often with some fuzziness. To evaluate DMU with fuzzy input/output data, researchers provided fuzzy data envelopment analysis (FDEA) model and proposed related evaluating method. But up to now, we still cannot evaluate a fuzzy sample decision making unit (SDMU) for FDEA model. So this paper proposes a generalized fuzzy DEA model which can evaluate a sample decision making unit and a numerical experiment is used to illustrate this model.

Efficiency measurement in data envelopment analysis in the presence of ordinal and interval data

2016 ◽  
Vol 30 (6) ◽  
pp. 1971-1982 ◽  
Author(s):  
Bohlool Ebrahimi ◽  
Madjid Tavana ◽  
Morteza Rahmani ◽  
Francisco J. Santos-Arteaga
Keyword(s):  
Data Envelopment Analysis ◽  
Interval Data ◽  
Efficiency Measurement ◽  
Data Envelopment

Data Envelopment Analysis Used in Taiwan Performance Evaluation of Highway Maintenance

2011 ◽  
Vol 250-253 ◽  
pp. 1675-1680
Author(s):  
Kuang Yi Wei ◽  
Jr Hung Peng ◽  
Jyh Dong Lin ◽  
Cin Rong Ciou
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Input Data ◽  
Data Envelopment ◽  
Road Maintenance ◽  
Highway Maintenance ◽  
Output Data ◽  
The Road ◽  
Decision Making Unit

DEA (Data Envelopment Analysis), Charnes has been published since 1978, due to the method of the correct value of properties in recent years have taken full advantage in the corporate performance assessment. In this study, each road maintenance units in Taiwan is to be a Decision Making Unit, the last two years of each DMU manpower, equipment number, maintenance expenses, etc., as input data, and score above the level units as output data, to analyze the performance of road maintenance authority, then proposed suggestions for improvement to the less efficient units, it can be a reference for follow-up work to enhance the efficiency of the road to the maintenance unit.

scholarly journals Combinatorial Efficiency Evaluation: The Knapsack Problem in Data Envelopment Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Xiao-guang Qi ◽  
Bo Guo
Keyword(s):  
Data Envelopment Analysis ◽  
Local Search ◽  
Knapsack Problem ◽  
Search Algorithm ◽  
Decision Makers ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Local Search Algorithm ◽  
Proposed Model ◽  
Decision Making Unit

The traditional data envelopment analysis (DEA) literatures generally concentrated on the efficiency evaluation of single decision making unit (DMU). However, in many practical problems, the decision makers are required to choose a number of DMUs instead of a single one from the DMUs set. Therefore, it is necessary to study the combinatorial efficiency evaluation problem which can be illustrated as a knapsack problem naturally. It is indicated that the basic model proposed by Cook and Green may have some drawbacks and a modified model, which is combined with the super efficiency model, is proposed in this paper. What is more, our proposed model is more persuasive to the decision makers because it is able to provide a unique best combination of DMUs. An adapted local search algorithm is developed as a solver of this problem. Finally, numerical examples are provided to examine the validity of our proposed model and the adapted local search algorithm.

scholarly journals Allocating fixed resources for DMUs with interval data

2020 ◽  
Author(s):  
Jiasen Sun ◽  
Meng Chen ◽  
Yelin FU ◽  
Hao Luo
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Interval Data ◽  
Decision Makers ◽  
Allocation Scheme ◽  
Output Data ◽  
Initial Allocation ◽  
Common Weights ◽  
Dea Models ◽  
Decision Making Units

Conventional DEA models tend to allocate the fixed resources to multiple decision-making units (DMUs) and treat the allocated resource as an extra input for every single DMU. However, the existing DEA resource allocation (DEA-RA) methods are applicable exclusively to the DMUs with exact values of inputs and outputs. A lack of precision for the input or output data of DMUs, such as the interval data, would cause a failure of the existing methods to allocate resources to DMUs. In order to resolve this problem, three DEA-RA models are proposed in this paper for different scenarios of decision-making. All of the proposed DEA-RA models are based on a set of common weights. Finally, the proposed models are empirically tested and validated through three examples. As revealed by the results, our proposed models are capable of providing a more fair and practical initial allocation scheme for decision makers.

Sign in / Sign up

Export Citation Format

Share Document