On the Calculation of Directional Scale Elasticity in Data Envelopment Analysis

2016 ◽  
Vol 33 (04) ◽  
pp. 1650026
Author(s):  
Mahdi Mirjaberi ◽  
Reza Kazemi Matin
Keyword(s):  
Data Envelopment Analysis ◽  
Decision Maker ◽  
Cross Sections ◽  
Optimization Algorithm ◽  
Parametric Optimization ◽  
Geometric Interpretation ◽  
Data Envelopment ◽  
Scale Elasticity

In recent years, the notion of scale elasticity—the relative changes of outputs with respect to relative changes of inputs—has received a lot of ink in the literature. However, all prior studies, except a few of them, assume that changes are equi-proportional. This simplifying assumption makes scale elasticity measure to preserve the throughput mix and implicitly ignores both input independencies and decision-maker preferences. This paper seeks to investigate two main objectives. It initially proposes the notion of directional scale elasticity to allow evaluation of elasticity in any direction which may naturally alter the mix. Subsequently, by providing a tangible geometric interpretation, it attempts to clarify the measure. Relevant computations are used to construct cross sections of frontier in a given direction by enlisting the advantages of parametric optimization algorithm.

scholarly journals Multicriteria approach to data envelopment analysis

2008 ◽  
Vol 28 (2) ◽  
pp. 231-242 ◽  
Author(s):  
Hélcio Vieira Junior
Keyword(s):  
Data Envelopment Analysis ◽  
Decision Maker ◽  
Decision Aids ◽  
Data Envelopment ◽  
Electre Iii ◽  
Multicriteria Decision ◽  
Dea Model ◽  
Unique Outcome ◽  
Weights Restrictions ◽  
Multicriteria Decision Aids

With the aim of making Data Envelopment Analysis (DEA) more acceptable to the managers' community, the Weights Restrictions approaches were born. They allow DEA to not dispose of any data and permit the Decision Maker (DM) to have some management over the method. The purpose of this paper is to suggest a Weights Restrictions DEA model that incorporates the DM preference. In order to perform that, we employed the MACBETH methodology as a tool to find out the bounds of the weights to be used in a Weights Restrictions approach named Virtual Weights Restrictions. Our proposal achieved an outcome that has an expressive correlation with three widely used decision-aids methodologies: the ELECTRE III, the SMART and the PROMETHEE I and II. In addition, our approach was able to join the most significant outcomes of all the above three Multicriteria decision-aids methodologies in one unique outcome.

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.

A simple derivation of scale elasticity in data envelopment analysis

2009 ◽  
Vol 197 (1) ◽  
pp. 149-153 ◽  
Author(s):  
Victor V. Podinovski ◽  
Finn R. Førsund ◽  
Vladimir E. Krivonozhko
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Simple Derivation ◽  
Scale Elasticity

Collaborative Design Using Trade-Off Satisfying Method in Supervisor System

2000 ◽  
Author(s):  
Tomoyuki Miyashita ◽  
Hiroshi Yamakawa
Keyword(s):  
Data Envelopment Analysis ◽  
Decision Maker ◽  
Collaborative Design ◽  
Evaluation Criteria ◽  
The Other ◽  
Data Envelopment ◽  
Trade Off ◽  
Numerical Examples ◽  
Financial Difficulties ◽  
The Cost

Abstract Recent years, financial difficulties led engineers to look for not only the efficiency of the function of a product but also the cost of its development. In order to reduce the time for the development, engineers in each discipline have to develop and improve their objectives collaboratively. Sometimes, they have to cooperate with those who have no knowledge at all for their own disciplines. Collaborative designs have been studied to solve these kinds of the problems, but most of them need some sorts of negotiation among disciplines and assumes that these negotiations will be done successfully. However, in the most cases of real designs, manager of each discipline does not want to give up his or her own objectives to stress on the other objectives. In order to carry out these negotiations smoothly, we need some sort of evaluation criteria which will show efficiency of the product considering the designs by each division and if possible, considering the products of the competitive company, too. In this study, we use Data Envelopment Analysis (DEA) to calculate the efficiency of the design and showed every decision maker the directions of the development of the design. We will call here these kinds of systems as supervisor systems and implemented these systems in computer networks that every decision maker can use conveniently. Through simple numerical examples, we showed the effectiveness of the proposed method.

Integrated data envelopment analysis and multicriteria decision-making ranking approach based on peer-evaluations and subjective preferences: case study in banking sector

2020 ◽  
Vol 54 (4) ◽  
pp. 551-582
Author(s):  
Jolly Puri ◽  
Meenu Verma
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Decision Maker ◽  
Multicriteria Decision Making ◽  
Peer Evaluation ◽  
Data Envelopment ◽  
Content Type ◽  
Multicriteria Decision ◽  
Subjective Preference ◽  
The Impact

PurposeThis paper is focused on developing an integrated algorithmic approach named as data envelopment analysis and multicriteria decision-making (DEA-MCDM) for ranking decision-making units (DMUs) based on cross-efficiency technique and subjective preference(s) of the decision maker.Design/methodology/approachSelf-evaluation in data envelopment analysis (DEA) lacks in discrimination power among DMUs. To fix this, a cross-efficiency technique has been introduced that ranks DMUs based on peer-evaluation. Different cross-efficiency formulations such as aggressive and benevolent and neutral are available in the literature. The existing ranking approaches fail to incorporate subjective preference of “one” or “some” or “all” or “most” of the cross-efficiency evaluation formulations. Therefore, the integrated framework in this paper, based on DEA and multicriteria decision-making (MCDM), aims to present a ranking approach to incorporate different cross-efficiency formulations as well as subjective preference(s) of decision maker.FindingsThe proposed approach has an advantage that each of the aggressive, benevolent and neutral cross-efficiency formulations contribute to select the best alternative among the DMUs in a MCDM problem. Ordered weighted averaging (OWA) aggregation is applied to aggregate final cross-efficiencies and to achieve complete ranking of the DMUs. This new approach is further illustrated and compared with existing MCDM approaches like simple additive weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prove its validity in real situations.Research limitations/implicationsThe choice of cross-efficiency formulation(s) as per subjective preference of the decision maker and different orness levels lead to different aggregated scores and thus ranking of the DMUs accordingly. The proposed ranking approach is highly useful in real applications like R and D projects, flexible manufacturing systems, electricity distribution sector, banking industry, labor assignment and the economic environmental performances for ranking and benchmarking.Practical implicationsTo prove the practical applicability and robustness of the proposed integrated DEA-MCDM approach, it is applied to top twelve Indian banks in terms of three inputs and two outputs for the period 2018–2019. The findings of the study (1) ensure the impact of non-performing assets (NPAs) on the ranking of the selected banks and (2) are enormously valuable for the bank experts and policy makers to consider the impact of peer-evaluation and subjective preference(s) in formulating appropriate policies to improve performance and ranks of underperformed banks in competitive scenario.Originality/valueTo the best of the authors’ knowledge, this is the first study that has integrated both DEA and MCDM via OWA aggregation to present a ranking approach that can incorporate different cross-efficiency formulations and subjective preference(s) of the decision maker for ranking DMUs.

scholarly journals Directional scale elasticity considering the management preference of decision-makers

2021 ◽  
Author(s):  
Tiantian REN ◽  
Zhongbao Zhou ◽  
Ruiyang Li ◽  
Wenbin Liu
Keyword(s):  
Data Envelopment Analysis ◽  
Returns To Scale ◽  
Basic Research ◽  
Decision Makers ◽  
Data Envelopment ◽  
Scale Elasticity ◽  
Chinese Academy Of Sciences ◽  
Simplifying Assumptions ◽  
Academy Of Sciences ◽  
Simultaneous Change

Most data envelopment analysis (DEA) studies on scale elasticity (SE) and returns to scale (RTS) of efficient units arise from the traditional definitions of them in economics, which is based on measuring radial changes in outputs caused by the simultaneous change in all inputs. In actual multiple inputs/outputs activities, the goals of expanding inputs are not only to obtain increases in outputs, but also to expect the proportions of such increases consistent with the management preference of decision-makers. However, the management preference is usually not radial changes in outputs. With the latter goal into consideration, this paper proposes the directional SE and RTS in a general formula for multi-output activities, and offers a DEA-based model for the formula of directional SE at any point on the DEA frontier, which is straightforward and requires no simplifying assumptions. Finally, the empirical part employs the data of 16 basic research institutions in Chinese Academy of Sciences (CAS) to illustrate the superiority of the proposed theories and methods.

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