Embedding OWA under preference ranking for DEA cross-efficiency aggregation: Issues and procedures

Oukil, Amar

doi:10.1002/int.22082

Cited by 36 publications

(10 citation statements)

References 57 publications

(101 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A future research direction may consist in considering intertemporal variations in the PRs of CPs and investigating potential ways of integrating this case into a DEA framework. Another avenue that also deserves to be explored is generating the PR weights by implicating more formally the CPs into the process through DEA cross‐efficiency (e.g., Oukil, ; Oukil & Amin, ). See Hassan and Oukil () and Oukil and Govindaluri () for recent applications.…”

Section: Resultsmentioning

confidence: 99%

A hybrid multi‐attribute decision‐making procedure for ranking project proposals: A historical data perspective

Oukil

Govindaluri

2019

Manage Decis Econ

Self Cite

View full text Add to dashboard Cite

This paper develops a hybrid multiattribute decision‐making methodology for ranking project proposals (PPs) through a judicious usage of historical data of completed projects to determine attribute weights, enabling elimination of problems associated with projected data such as cost and schedule overruns of real‐world projects. The weights generated from data envelopment analysis are explicitly utilized for ranking PPs while allowing subjectivity to be ingeniously incorporated into the decision process. The new approach is implemented for ranking 25 PPs, and the rankings it yields are found indifferent to the decision maker's attitude, which ascertains the robustness of the ranking methodology.

show abstract

Section: Resultsmentioning

confidence: 99%

A hybrid multi‐attribute decision‐making procedure for ranking project proposals: A historical data perspective

Oukil

Govindaluri

2019

Manage Decis Econ

Self Cite

View full text Add to dashboard Cite

show abstract

“…For future directions, it is noted that the secondary goals in light of promoting balance could be extended to other forms such as incorporating with DMs' preference structure, considering the competitive relationships among DMUs [24], or the two-stage [25] and network [26] systems. Another extension lies in that the proposed approach uses the simple averaging for cross-efficiency aggregation and this can be extended to other choices [27] [28].…”

Section: Discussionmentioning

confidence: 99%

Promoting Balance in Output Efficiencies for Cross-Efficiency Evaluation in Data Envelopment Analysis

Chen¹

2019

JAMP

View full text Add to dashboard Cite

Cross-efficiency evaluation is recognized as an effective way of efficiency assessment for a set of decision making units (DMUs) in the framework of data envelopment analysis (DEA). It has been generally suggested that secondary goals be introduced for cross-efficiency evaluation owing to the non-uniqueness of optimal solutions in self-evaluation. This paper develops a variety of secondary goals in the spirit of promoting balance in the output efficiencies of the DMU under evaluation. The proposed models attempt to make each output contribute as equally as possible to the self-evaluated efficiency. In this way, the weight flexibility can for one thing be reduced by the introduced secondary goals with selections from alternate optimal solutions, in addition to counting on the dilution of flexibility in the subsequent peer-evaluation. The proposed approach might be applicable to evaluation problems in which multiple outputs are considered important and balance is encouraged to put all dimensions into sufficient use. The effectiveness of the proposed approach and its comparisons with some relevant secondary goals are illustrated empirically using numerical examples.

show abstract

“…However, minimax disparity approach involves a linear programming problem that can easily be solved by using existing softwares such as LINGO, LINDO, MATLAB, etc. Many researchers have considered minimax disparity approach over other approaches to determine OWA operator weights in cross-efficiency aggregation which can be seen in the reports like Wang and Chin (2011), León et al (2014), Sanei and Banihashemi (2014), Amin and Hajjami (2016), Oukil and Govindaluri (2017) and Oukil (2019).…”

Section: Preliminariesmentioning

confidence: 99%

“…The idea of combining DEA with OWA is not new. Many researchers have integrated DEA and OWA that can be seen in the reports like León et al (2014), Amin and Hajjami (2016), Oukil and Govindaluri (2017), Carrillo and Jorge (2018), Oukil (2019) and Dursun and Goker (2019).…”

Section: Introductionmentioning

confidence: 99%

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

Puri

Verma

2020

DTA

View full text Add to dashboard Cite

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.

show abstract

Embedding OWA under preference ranking for DEA cross-efficiency aggregation: Issues and procedures

Cited by 36 publications

References 57 publications

A hybrid multi‐attribute decision‐making procedure for ranking project proposals: A historical data perspective

A hybrid multi‐attribute decision‐making procedure for ranking project proposals: A historical data perspective

Promoting Balance in Output Efficiencies for Cross-Efficiency Evaluation in Data Envelopment Analysis

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

Contact Info

Product

Resources

About