Using Induced Ordered Weighted Averaging (IOWA) Operators for Aggregation in Cross-Efficiency Evaluations

León, Teresa; Ramon, Núria Tous; Ruiz, José L.; Sirvent, Inmaculada

doi:10.1002/int.21685

Cited by 21 publications

(9 citation statements)

References 36 publications

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“…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%

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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

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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.

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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

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“…Constructing a majority opinion could be explained as “... the collective evaluation of a majority of the agents involved in the decision problem.” The following authors provide ample and detailed information about the OWA operators and its applications: León et al . and Pérez et al.…”

Section: The Proposed Iowa Approach To Sentiment Analysismentioning

confidence: 99%

A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority

Appel

Chiclana

Carter

et al. 2017

Int. J. Intell. Syst.

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In group decision making, there are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnose of an illness or parliament members looking for consensus on an specific law being passed. In this article, we present a method that utilizes induced ordered weighted averaging (IOWA) operators to aggregate a majority opinion from a number of sentiment analysis (SA) classification systems, where the latter occupy the role usually taken by human decision‐makers as typically seen in group decision situations. In this case, the numerical outputs of different SA classification methods are used as input to a specific IOWA operator that is semantically close to the fuzzy linguistic quantifier ‘most of’. The object of the aggregation will be the intensity of the previously determined sentence polarity in such a way that the results represent what the majority think. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier ‘most’ (IOWAmost) proved to yield superior results compared to those achieved when utilizing other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques.

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“…In addition, Ramón [11], [12], Wang et al [13], [14], Wu et al [15] used secondary goals to get the weights which effectively avoid the problem of zero weight. For cross-efficiency aggregation problems, previous studies obtained the weight of each cross-efficiency to summarize the cross-efficiency by using nuclear and Shapley values [16], [17], Shannon entropy [18]- [20], ordered weighted average operators [21], evidence reasoning methods [22], Gini Guidelines [23] and the ordered weighted averaging (OWA) [24], [25]. In addition, Liang et al [26] proposed the game cross-efficiency model in which DMUs were regarded as players in the non-cooperative game, Wu et al [27] applied to the performance evaluation of participating countries in the Olympic Games.…”

Section: Introductionmentioning

confidence: 99%

An Improvement of DEA Cross-Efficiency Aggregation Based on BWM-TOPSIS

Jian

Guo

2020

IEEE Access

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Data Envelopment Analysis (DEA) cross-efficiency has been used to replace the self-evaluation system, which requires decision-makers to rank a series of decision-making units (DMUs) according to cross-efficiency scores, and finally determine the order of each DMU, so as to provide decision-making basis for decision-makers. However, this method has certain deficiencies: one is that the cross-efficiency value is not unique; the other one is that the cross-efficiency evaluation uses the same weight to aggregate cross-efficiency. In this paper, Technique for order performance by similarity to ideal solution (TOPSIS) and The Best Worst Method (BWM) methods are used to aggregate the aggressive cross-efficiency values and benevolent cross-efficiency values. Besides, the high consistency of the BWM method improves the shortcomings of the TOPSIS method. The result of TOPSIS method is not necessarily close to the ideal solution, and far away from the negative ideal solution. At the same time, the distance measured by the TOPSIS method replaces the preference of the BWM method, which makes BWM method more objective. Finally, a numerical example is given to verify the feasibility and effectiveness of the method. INDEX TERMS Cross-efficiency aggregation, data envelopment analysis, best worst method, TOPSIS.

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Using Induced Ordered Weighted Averaging (IOWA) Operators for Aggregation in Cross-Efficiency Evaluations

Cited by 21 publications

References 36 publications

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

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

A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority

An Improvement of DEA Cross-Efficiency Aggregation Based on BWM-TOPSIS

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