scholarly journals Finding the Most Preferred Decision-Making Unit in Data Envelopment Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Shirin Mohammadi ◽  
S. Morteza Mirdehghan ◽  
Gholamreza Jahanshahloo
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Utility Function ◽  
Decision Maker ◽  
Programming Models ◽  
Data Envelopment ◽  
Production Possibility ◽  
Decision Making Unit ◽  
Inputs And Outputs

Data envelopment analysis (DEA) evaluates the efficiency of the transformation of a decision-making unit’s (DMU’s) inputs into its outputs. Finding the benchmarks of a DMU is one of the important purposes of DEA. The benchmarks of a DMU in DEA are obtained by solving some linear programming models. Currently, the obtained benchmarks are just found by using the information of the data of inputs and outputs without considering the decision-maker’s preferences. If the preferences of the decision-maker are available, it is very important to obtain the most preferred DMU as a benchmark of the under-assessment DMU. In this regard, we present an algorithm to find the most preferred DMU based on the utility function of decision-maker’s preferences by exploring some properties on that. The proposed method is constructed based on the projection of the gradient of the utility function on the production possibility set’s frontier.

scholarly journals Directional Slack-Based Measure for the Inverse Data Envelopment Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ali Mirsalehy ◽  
Mohd Rizam Abu Bakar ◽  
Lai Soon Lee ◽  
Azmi B. Jaafar ◽  
Maryam Heydar
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Efficiency Score ◽  
Data Envelopment ◽  
New Production ◽  
Production Possibility ◽  
Novel Technique ◽  
Decision Making Unit ◽  
Measure Model ◽  
Production Possibility Set

A novel technique has been introduced in this research which lends its basis to the Directional Slack-Based Measure for the inverse Data Envelopment Analysis. In practice, the current research endeavors to elucidate the inverse directional slack-based measure model within a new production possibility set. On one occasion, there is a modification imposed on the output (input) quantities of an efficient decision making unit. In detail, the efficient decision making unit in this method was omitted from the present production possibility set but substituted by the considered efficient decision making unit while its input and output quantities were subsequently modified. The efficiency score of the entire DMUs will be retained in this approach. Also, there would be an improvement in the efficiency score. The proposed approach was investigated in this study with reference to a resource allocation problem. It is possible to simultaneously consider any upsurges (declines) of certain outputs associated with the efficient decision making unit. The significance of the represented model is accentuated by presenting numerical examples.

scholarly journals Using goal programming method to solve DEA problems with value judgments

2014 ◽  
Vol 24 (2) ◽  
pp. 267-282 ◽  
Author(s):  
M. Izadikhah ◽  
R. Roostaee ◽  
F. Hosseinzadehlotfi
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Decision Maker ◽  
Goal Programming ◽  
Programming Method ◽  
Programming Approach ◽  
Multiple Objective Linear Programming ◽  
Data Envelopment ◽  
Value Judgments

Data envelopment analysis (DEA) is a linear programming approach for measuring relative efficiency of peer decision making units that have multiple inputs and outputs. DEA was developed without consideration of the decision maker?s preference structures. DEA and multiple objective linear programming are tools that can be used in management control and planning. This paper shows how a data envelopment analysis problem can be solved by transforming it into MOLP formulation. We use the goal programming method to reflect the decision making preferences in the process of assessing efficiency, such that the value judgments of the decision maker are considered. Therefore, the proposed method can find a solution that satisfies the decision maker?s goal levels. A case study is provided to illustrate how data envelopment oriented efficiency analysis can be conducted by using goal programming method.

scholarly journals Airports economic efficient frontier

2018 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Decio Yoshimoto ◽  
Cláudio Jorge Pinto Alves ◽  
Mauro Caetano
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Production Function ◽  
Programming Model ◽  
Efficient Frontier ◽  
Data Envelopment ◽  
International Airport ◽  
Decision Making Unit ◽  
Terminal Area

<p>Studies about airport operational efficiency models generally disregard the correlation between operational efficiencies and economic drivers. The goal of this study is, firstly, to isolate and detail the key economic drivers and then find their efficient frontier. The methodology employed was Data Envelopment Analysis (DEA) as a non-parametric and linear programming model. It provides relative measures of efficiency using multiple inputs and outputs for a given Decision-Making Unit (DMU) without requiring a prior production function. The number of variables in this study was limited in function of the DMUs analyzed, which consisted of the following Brazilian airports: Congonhas Airport (CGH), Guarulhos International Airport (GRU) and Viracopos International Airport (VCP). Two of the airports, GRU and VCP, were found to be efficient considering this study’s combination of very limited variables, meaning that theses airports, from this isolated standpoint, are maximizing their commercial, passenger parking and marketing revenues, given their terminal area and the number of yearly passengers.</p>

Evaluation of Heuristics Using Data Envelopment Analysis

2014 ◽  
Vol 13 (04) ◽  
pp. 795-810 ◽  
Author(s):  
Chung-Cheng Jason Lu ◽  
Yen-Chun Jim Wu
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Data Envelopment Analysis ◽  
Statistical Tests ◽  
Data Envelopment ◽  
Decision Making Unit ◽  
Dea Models ◽  
Using Data ◽  
Inputs And Outputs

This paper focuses on identifying relatively efficient configurations of algorithmic operators among a set of configurations in the development of heuristics or meta-heuristics. Each configuration is considered as a decision-making unit with multiple inputs and outputs. Then, data envelopment analysis (DEA) is adopted to evaluate relative and cross-efficiencies of a set of algorithmic configurations. The proposed approach differs from existing methods based on statistical tests in that multiple inputs and outputs are simultaneously considered in an integrated framework for the evaluation of algorithmic efficiency. A case study is presented to demonstrate the application of DEA for determining the efficient configurations of genetic algorithm operators. The evaluation results of two DEA models are also compared. The DEA evaluation results are consistent with those obtained by a commonly used statistical method.

scholarly journals Data Envelopment Analysis - Basic Models and their Utilization

Organizacija ◽  
2009 ◽  
Vol 42 (2) ◽  
pp. 37-43 ◽  
Author(s):  
Milan Martić ◽  
Marina Novaković ◽  
Alenka Baggia
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Efficiency Frontier ◽  
Dea Models ◽  
Decision Making Tool ◽  
Inputs And Outputs

Data Envelopment Analysis - Basic Models and their UtilizationData Envelopment Analysis (DEA) is a decision making tool based on linear programming for measuring the relative efficiency of a set of comparable units. Besides the identification of relatively efficient and inefficient units, DEA identifies the sources and level of inefficiency for each of the inputs and outputs. This paper is a survey of the basic DEA models. A comparison of DEA models is given. The effect of model orientation (input or output) on the efficiency frontier and the effect of the convexity requirements on returns to scale are examined. The paper also explains how DEA models can be used to assess efficiency.

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 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.

scholarly journals A Full Ranking for Decision Making Units Using Ideal and Anti-Ideal Points in DEA

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
A. Barzegarinegad ◽  
G. Jahanshahloo ◽  
M. Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Real Data ◽  
The Other ◽  
Data Envelopment ◽  
Production Possibility ◽  
Decision Making Unit ◽  
Common Set Of Weights ◽  
Production Possibility Set ◽  
Ideal Points ◽  
Decision Making Units

We propose a procedure for ranking decision making units in data envelopment analysis, based on ideal and anti-ideal points in the production possibility set. Moreover, a model has been introduced to compute the performance of a decision making unit for these two points through using common set of weights. One of the best privileges of this method is that we can make ranking for all decision making units by solving only three programs, and also solving these programs is not related to numbers of decision making units. One of the other advantages of this procedure is to rank all the extreme and nonextreme efficient decision making units. In other words, the suggested ranking method tends to seek a set of common weights for all units to make them fully ranked. Finally, it was applied for different sets holding real data, and then it can be compared with other procedures.

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