scholarly journals Developing Common Set of Weights with Considering Nondiscretionary Inputs and Using Ideal Point Method

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Reza Kiani Mavi ◽  
Sajad Kazemi ◽  
Jay M. Jahangiri
Keyword(s):  
Decision Making ◽  
Ideal Point ◽  
Main Idea ◽  
Efficiency Score ◽  
Point Method ◽  
Bank Branches ◽  
Proposed Model ◽  
Decision Making Unit ◽  
Common Set Of Weights ◽  
Nondiscretionary Inputs

Data envelopment analysis (DEA) is used to evaluate the performance of decision making units (DMUs) with multiple inputs and outputs in a homogeneous group. In this way, the acquired relative efficiency score for each decision making unit lies between zero and one where a number of them may have an equal efficiency score of one. DEA successfully divides them into two categories of efficient DMUs and inefficient DMUs. A ranking for inefficient DMUs is given but DEA does not provide further information about the efficient DMUs. One of the popular methods for evaluating and ranking DMUs is the common set of weights (CSW) method. We generate a CSW model with considering nondiscretionary inputs that are beyond the control of DMUs and using ideal point method. The main idea of this approach is to minimize the distance between the evaluated decision making unit and the ideal decision making unit (ideal point). Using an empirical example we put our proposed model to test by applying it to the data of some 20 bank branches and rank their efficient units.

scholarly journals Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 813-832
Author(s):  
Sajad Kazemi ◽  
Reza Kiani Mavi ◽  
Ali Emrouznejad ◽  
Neda Kiani Mavi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Ideal Point ◽  
Weight Vector ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Complex Organizations ◽  
Measuring Efficiency ◽  
Common Set Of Weights ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster.

Sustainable efficiency assessment of private diagnostic laboratories under uncertainty

2020 ◽  
Vol 15 (3) ◽  
pp. 1069-1103
Author(s):  
Niloufar Ghafari Someh ◽  
Mir Saman Pishvaee ◽  
Seyed Jafar Sadjadi ◽  
Roya Soltani
Keyword(s):  
Decision Making ◽  
Sustainable Development ◽  
Managerial Decision ◽  
Diagnostic Laboratory ◽  
Content Type ◽  
Development Indicators ◽  
Sustainable Development Indicators ◽  
Proposed Model ◽  
Decision Making Unit ◽  
Medical Diagnostic

Purpose Assessing the performance of medical laboratories plays an important role in the quality of health services. However, because of imprecise data, reliable results from laboratory performance cannot be obtained easily. The purpose of this paper is to illustrate the use of interval network data envelopment analysis (INDEA) based on sustainable development indicators under uncertainty. Design/methodology/approach In this study, each medical diagnostic laboratory is considered as a decision-making unit (DMU) and an INDEA model is used for calculating the efficiency of each medical diagnostic laboratory under imprecise inputs and outputs. The proposed model helps provide managers with effective performance scores for deficiencies and business improvements. The proposed model with realistic efficiency scores can help administrators manage their deficiencies and ultimately improve their business. Findings The results indicate that uncertainty can lead to changes in performance scores, rankings and performance classifications. Therefore, the use of DEA models under certainty can be potentially misleading. Originality/value The contribution of this study provides useful insights into the use of INDEA as a modeling tool to aid managerial decision-making in assessing efficiency of medical diagnostic laboratories based on sustainable development indicators under uncertainty.

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.

scholarly journals An algorithm for the anchor points of the PPS of the FRH models

2020 ◽  
Author(s):  
Dariush Akbarian
Keyword(s):  
Decision Making ◽  
Sufficient Conditions ◽  
Data Envelopment ◽  
Proposed Model ◽  
Decision Making Unit ◽  
Production Possibility Set ◽  
Anchor Points ◽  
Definition Of ◽  
Necessary And Sufficient ◽  
Decision Making Units

In this paper we deal with a variant of non-convex data envelopment analysis, called free replication hull model and try to obtain their anchor points. This paper uses a variant of super-efficiency model to characterize all extreme efficient decision making units and anchor points of the free replication hull models. A necessary and sufficient conditions for a decision making unit to be anchor point of the production possibility set of the free replication hull models are stated and proved. Since the set of anchor points is a subset of the set of extreme units, a definition of extreme units and a new method for obtaining these units in non-convex technologies are given. To illustrate the applicability of the proposed model, some numerical examples are finally provided.

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.

The Application of Entropy Weighting Ideal Point Method in Electronic Information Equipment System of Systems Construction Decision-Making

2013 ◽  
Vol 385-386 ◽  
pp. 1813-1818
Author(s):  
Zhi Qiang Zhao ◽  
Zhi Gang Wang
Keyword(s):  
Decision Making ◽  
Ideal Point ◽  
Practical Method ◽  
System Of Systems ◽  
Point Method ◽  
Electronic Information ◽  
Entropy Weight ◽  
Subjective Weight ◽  
Construction Risk ◽  
Equipment System

The equipment system of systems (ESoS) construction risk analysis is great significance to enhance decision-making and management level of information technology ESoS construction. The concept of "entropy" in the information engineering is using to calculate the entropy, and the entropy weight is used to fix subjective weight, after that Combining ideal point method to evaluate and select the schemes, the best decision of ESoS construction is get. The model is of impersonality and in reason, which provided a simple and practical method for construction risk evaluation of electronic information ESoS.

Evaluating after-sales service units by developing inverse network data envelopment analysis model

2019 ◽  
Vol 27 (2) ◽  
pp. 695-707 ◽  
Author(s):  
Reza Farzipoor Saen ◽  
Seyed Shahrooz Seyedi Hosseini Nia
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Efficiency Score ◽  
Data Envelopment Analysis Model ◽  
Network Data ◽  
Data Envelopment ◽  
Analysis Model ◽  
Content Type ◽  
Network Data Envelopment Analysis ◽  
Decision Making Unit

Purpose The purpose of this paper is to develop an inverse network data envelopment analysis (INDEA) model to solve resource allocation problems. Design/methodology/approach The authors estimate inputs’ variations based on outputs so that the efficiencies of decision-making unit under evaluation (DMUo) and other decision-making units (DMUs) are constant. Findings The new INDEA model is developed to allocate resources such that inputs are not increased while efficiency scores of all DMUs remain constant. Furthermore, the authors obtain new combinations of inputs and outputs, together with a growth in efficiency score of DMUo such that efficiency scores of other DMUs are not changed. A case study is provided. Originality/value This paper proposes INDEA model to estimate inputs (outputs) without changing efficiency scores of DMUs.

DEA Cross-Efficiency Evaluation Method Based on Good Relationship

2015 ◽  
Vol 3 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Ruijuan Guo ◽  
Yanling Dong ◽  
Wang Meiqiang ◽  
Li Yongjun
Keyword(s):  
Decision Making ◽  
Evaluation Method ◽  
Efficiency Score ◽  
Interval Number ◽  
Efficiency Evaluation ◽  
Good Relationship ◽  
Self Evaluation ◽  
Decision Making Unit ◽  
Interval Efficiency ◽  
Decision Making Units

AbstractBenevolent and aggressive cross-efficiency evaluation methods are the improvement of the cross-efficiency evaluation method. They merely maximize or minimize the efficiency of the composite unit constructed by evaluated decision-making units while maintaining the optimal efficiency of the decision-making unit under evaluation. The two methods completely ignore the self-evaluation efficiency of evaluated unit and the good relationship among decision-making units. To solve the above drawbacks, the authors consider the efficiency score of the decision-making unit as an interval number and propose a more reasonable interval number. On the basis of the interval efficiency, the authors provide the benevolent and aggressive DEA cross-efficiency evaluation models based on the good relationship among all decision-making units. Finally, a numerical example is provided to illustrate the proposed method.

scholarly journals Dynamic Energy Efficiency, Energy Decoupling Rate, and Decarbonization: Evidence from ASEAN+6

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110472
Author(s):  
Chiang-Ping Chen ◽  
Ming-Chung Chang ◽  
Wei-Che Tsai
Keyword(s):  
Decision Making ◽  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Behavior Change ◽  
The Other ◽  
Data Envelopment ◽  
Dynamic Efficiency ◽  
Energy Usage ◽  
Proposed Model ◽  
Decision Making Unit

Improving energy efficiency is widely identified as shifting energy usage to an optimal status in order to ultimately strengthen a country’s competitiveness and development, and indeed this is of particular relevance to the Association of Southeast Asian Nations plus Six’ (ASEAN+6). The traditional data envelopment analysis (DEA) approach has extensively been employed for estimating energy efficiency, but it does not properly utilize the weight in the DEA model to probe the behavior change of a decision making unit (DMU). This research therefore applies a progressive time-weighted dynamic efficiency model (PTDEM) to estimate the energy efficiency of ASEAN+6 and discusses the issues concerning their energy decoupling rates and decarbonization. The proposed model herein fully considers a DMU’s behavior change by estimating its energy efficiency. Empirical results reveal that: (i) improvements in energy efficiency within ASEAN are greater than in the other six countries; (ii) members of ASEAN still have more room for improvement than the other six countries with regard to achieving the standard ratio of the energy decoupling rate; and (iii) there is no evidence of convergence to decarbonization within ASEAN+6.

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