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.

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.

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 Operational Efficiency of Chinese Provincial Electricity Grid Enterprises: An Evaluation Employing a Three-Stage Data Envelopment Analysis (DEA) Model

2018 ◽  
Vol 10 (9) ◽  
pp. 3168 ◽  
Author(s):  
Haoran Zhao ◽  
Huiru Zhao ◽  
Sen Guo
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Variables ◽  
Market Competition ◽  
Environmental Values ◽  
Operational Efficiency ◽  
Data Envelopment ◽  
Electricity Grid ◽  
Frontier Analysis ◽  
Dea Model ◽  
Statistical Noise

With the implementation of new round electricity system reform in China, the provincial electricity grid enterprises (EGEs) of China should focus on improving their operational efficiency to adapt to the increasingly fierce market competition and satisfy the requirements of the electricity industry reform. Therefore, it is essential to conduct operational efficiency evaluation on provincial EGEs. While considering the influences of exterior environmental variables on the operational efficiency of provincial EGEs, a three-stage data envelopment analysis (DEA) methodology is first utilized to accurately assess the real operational efficiency of provincial EGEs excluding the exterior environmental values and statistical noise. The three-stage DEA model takes the amount of employees, the fixed assets investment, the 110 kV and below distribution line length, and the 110 kV and below transformer capacity as input variables and the electricity sales amount, the amount of consumers, and the line loss rate as output variables. The regression results of the stochastic frontier analysis model indicate that the operational efficiencies of provincial EGEs are significantly affected by exterior environmental variables. Results of the three-stage DEA model imply that the exterior environmental values and statistical noise result in the overestimation of operational efficiency of provincial EGEs, and the exclusion of exterior environmental values and statistical noise has provincial-EGE-specific influences. Furthermore, 26 provincial EGEs are divided into four categories to better understand the differences of operational efficiencies before and after the exclusion of exterior environmental values and statistical noise.

scholarly journals A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs

2021 ◽  
Vol 9 (4) ◽  
pp. 378-398
Author(s):  
Chunhua Chen ◽  
Haohua Liu ◽  
Lijun Tang ◽  
Jianwei Ren
Keyword(s):  
Data Envelopment Analysis ◽  
Transportation Systems ◽  
Decision Makers ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Super Efficiency ◽  
Dea Model ◽  
Dea Models ◽  
Analysis Models ◽  
Transport Accidents

Abstract DEA (data envelopment analysis) models can be divided into two groups: Radial DEA and non-radial DEA, and the latter has higher discriminatory power than the former. The range adjusted measure (RAM) is an effective and widely used non-radial DEA approach. However, to the best of our knowledge, there is no literature on the integer-valued super-efficiency RAM-DEA model, especially when undesirable outputs are included. We first propose an integer-valued RAM-DEA model with undesirable outputs and then extend this model to an integer-valued super-efficiency RAM-DEA model with undesirable outputs. Compared with other DEA models, the two novel models have many advantages: 1) They are non-oriented and non-radial DEA models, which enable decision makers to simultaneously and non-proportionally improve inputs and outputs; 2) They can handle integer-valued variables and undesirable outputs, so the results obtained are more reliable; 3) The results can be easily obtained as it is based on linear programming; 4) The integer-valued super-efficiency RAM-DEA model with undesirable outputs can be used to accurately rank efficient DMUs. The proposed models are applied to evaluate the efficiency of China’s regional transportation systems (RTSs) considering the number of transport accidents (an undesirable output). The results help decision makers improve the performance of inefficient RTSs and analyze the strengths of efficient RTSs.

scholarly journals Sustainability Evaluation of Product Packaging System with a Three-Stage Network Data Envelopment Analysis Methodology

2019 ◽  
Vol 9 (2) ◽  
pp. 246 ◽  
Author(s):  
Yong Xie ◽  
Yafang Gao ◽  
Shihao Zhang ◽  
Hailong Bai ◽  
Zhenghao Liu
Keyword(s):  
Life Cycle ◽  
Data Envelopment Analysis ◽  
Empirical Test ◽  
Environmentally Friendly ◽  
Network Data ◽  
Data Envelopment ◽  
Packaging System ◽  
Product Packaging ◽  
Dea Model ◽  
Network Data Envelopment Analysis

This study presents a method that is based on the three-stage network Data Envelopment Analysis (DEA) to evaluate the sustainability of packaging systems for a product. This method facilitates the selection of better product packaging alternatives from an environmentally friendly point of view and it comprises the following four steps: (i) the definition of packaging sustainability indicator (PSI) based on environmental efficiency and impact indicator of three-stage in packaging life cycle, (ii) modeling a three-stage Network DEA model for a packaging system, (iii) computing PSI based on the DEA model, and (iv) result analysis. An empirical test has been progressed to prove the feasibility of the proposed method by selecting the three types of milk packaging systems. The results indicated that the PSI value of PrePack is the maximum and the Tetra Pak minimum. According to these results, the study provides an environmentally friendly evaluation method for product packaging systems, which is more intuitive than Life Cycle Assessment (LCA).

scholarly journals An Integrated Analytic Hierarchy Process—Slack Based Measure-Data Envelopment Analysis Model for Evaluating the Efficiency of Logistics Service Providers Considering Undesirable Performance Criteria

2019 ◽  
Vol 11 (8) ◽  
pp. 2330 ◽  
Author(s):  
Patricija Bajec ◽  
Danijela Tuljak-Suban
Keyword(s):  
Data Envelopment Analysis ◽  
Service Providers ◽  
Performance Criteria ◽  
Data Envelopment ◽  
Analytic Hierarchy ◽  
Dea Model ◽  
Logistics Service ◽  
Logistics Service Providers ◽  
Proposed Model ◽  
Hierarchy Process

Sustainable concerns are reputed to be of the utmost priority among governments. Consequently, they have become more and more of a concern among supply chain partners. Logistics service providers (LPs), as significant contributors to supply chain success but also one of the greatest generator of emissions, play a significant role in reducing the negative environmental impact. Thus, the performance evaluations of LPs should necessarily involve such a measure which, firstly, represents a balance between all three pillars of sustainability and, secondly, consider the desirable and undesirable performance criteria. This paper proposes an integrated analytic hierarchy process (AHP) and slack-based measure (SBM) data envelopment analysis (DEA) model, based on the assumption of a variable return to scale (VRS). An AHP pairwise comparison enables selecting the most influential input/output variables. Output-oriented SBM DEA provides simultaneously evaluation of both the undesirable and desirable outputs. The proposed model was tested on a numerical example of 18 LPs. The comparison of output Charnes, Cooper and Rhodes (CCR) and SBM DEA models resulted in a higher number of inefficient LPs when the SBM DEA model was applied. Moreover, efficiency scores of inefficient LPs were lower in SBM DEA model. The proposed model is fair to those LPs that are environmentally friendly.

A common-weights approach for efficiency evaluation in fuzzy data envelopment analysis with undesirable outputs: Application in banking industry

2020 ◽  
Vol 39 (5) ◽  
pp. 7705-7722
Author(s):  
Mohammad Kachouei ◽  
Ali Ebrahimnejad ◽  
Hadi Bagherzadeh-Valami
Keyword(s):  
Data Envelopment Analysis ◽  
Performance Management ◽  
Banking Industry ◽  
Evaluation Process ◽  
Fuzzy Arithmetic ◽  
Data Envelopment ◽  
Fuzzy Data ◽  
Undesirable Outputs ◽  
Dea Model ◽  
The Impact

Data Envelopment Analysis (DEA) is a non-parametric approach based on linear programming for evaluating the performance of decision making units (DMUs) with multiple inputs and multiple outputs. The lack of the ability to generate the actual weights, not considering the impact of undesirable outputs in the evaluation process and the measuring of efficiencies of DMUs based upon precise observations are three main drawbacks of the conventional DEA models. This paper proposes a novel approach for finding the common set of weights (CSW) to compute efficiencies in DEA model with undesirable outputs when the data are represented by fuzzy numbers. The proposed approach is based on fuzzy arithmetic which formulates the fuzzy additive DEA model as a linear programing problem and gives fuzzy efficiencies of all DMUs based on resulting CSW. We demonstrate the applicability of the proposed model with a simple numerical example. Finally, in the context of performance management, an application of banking industry in Iran is presented for analyzing the influence of fuzzy data and depicting the impact of undesirable outputs over the efficiency results.

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