Cross-efficiency aggregation method based on prospect consensus process

Chen, Lei; Wang, Ying‐Ming; Huang, Yan

doi:10.1007/s10479-019-03491-w

Cited by 44 publications

(10 citation statements)

References 44 publications

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“…Considering the subjective preference of decision makers, Chen et al. (2020b) construct a cross‐efficiency aggregation model based on the Prospect Theory. Wu et al.…”

Section: Introductionmentioning

confidence: 99%

“…Liang et al (2008a) and Wang and Chin (2010) sequentially develop some alternative secondary goals for the cross-efficiency evaluation. Other would include Liang et al (2008b), Ramón et al (2010Ramón et al ( , 2011, Wang et al (2012), Ruiz (2013), Wu et al (2016aWu et al ( , 2016b, Chen and Wang (2020), Chen et al (2020a), Contreras et al (2021), and among others.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the subjective preference of decision makers, Chen et al (2020b) construct a crossefficiency aggregation model based on the Prospect Theory. Wu et al (2021) develop a new composite method to rank DMUs by calculating the Shannon entropy of the obtained cross-efficiency scores derived from the perspectives of satisfaction and consensus.…”

Section: Introductionmentioning

confidence: 99%

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DEA cross‐efficiency aggregation based on preference structure and acceptability analysis

2021

Int Trans Operational Res

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Although a considerable amount of efforts have been made to overcome the drawbacks of traditional equal aggregation in the Data Envelopment Analysis cross‐efficiency evaluation, the endogenous preference structures in the cross‐efficiency matrix are still largely unexplored. We first untangle both preferential differences denoting the preference degrees among different Decision Making Unit (DMU), and preferential priorities denoting the favorite ranking of the DMUs. A revised cross‐efficiency matrix is formulated. We then apply the stochastic multicriteria acceptability analysis for group decision‐making method to obtain a set of holistic acceptability indices corresponding to different DMUs. Finally, we develop a satisfaction index to evaluate the satisfactory level of the proposed method. Two numerical examples with respect to the Flexible Manufacturing System selection problem and industrial robot selection problem are conducted to validate the proposed methodology. The superiority of our methodology is manifested in terms of an improvement of the overall satisfactory level as 18.69% and 8.48%, respectively.

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“…Considering the subjective preference of decision makers, Chen et al. (2020b) construct a cross‐efficiency aggregation model based on the Prospect Theory. Wu et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DEA cross‐efficiency aggregation based on preference structure and acceptability analysis

2021

Int Trans Operational Res

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“…Wang and Chin (2010) proposed neutral DEA model that determines one set of input and output weights for every DMU without being aggressive or benevolent to the others. Many researchers worked on the aggregation of the cross-efficiencies which can be seen in Wang and Chin (2011), Wu et al (2012), Yang et al (2013), Oukil and Amin (2015), Liu et al (2017), Carrillo and Jorge (2018), Liu et al (2019a), Chen et al (2020a) and Fan et al (2020).…”

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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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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“…Fang and Yang (2019) aggregate cross‐efficiency intervals based on the cumulative prospect theory. Chen, Wang, and Huang (2019) constructed a cross‐efficiency aggregation method based on the prospect consensus process. The above studies aggregate the DEA cross‐efficiency under the concept of prospect theory, but the DEA cross‐efficiency obtained from the existing DEA models still do not actually catch the non‐rational psychological behaviours of a decision maker under a risk scenario.…”

Section: Introductionmentioning

confidence: 99%

Common set of weights in data envelopment analysis under prospect theory

Zhu

Shi

et al. 2020

Expert Systems

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Data envelopment analysis (DEA) is a data-driven tool for performance evaluation, measuring decision-making units (DMUs) and designating them with specific weightings. The standard DEA model typically sets up that decision-makers (DMs) are wholly rational to select the most favourable weights to obtain the maximum performance score, but does not take into account their attitude toward risk during the assessment. The prospect theory generally matches humans' psychological behaviours. Thus, our study captures the non-rational behaviours of DMs, performing under risk scenarios, in order to construct a novel common-weights DEA model that maximizes the total prospect value, which can vary more steeply for losses than for gains, hence obtaining a more realistic common weight scheme. Our proposed model not only generates DMUs, with higher total prospect values, but also greater degrees of satisfaction. The current study shows that the prospect theory can be aptly extended to the DEA research area, supplying a proper guideline for future DEA research.

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Cross-efficiency aggregation method based on prospect consensus process

Cited by 44 publications

References 44 publications

DEA cross‐efficiency aggregation based on preference structure and acceptability analysis

DEA cross‐efficiency aggregation based on preference structure and acceptability analysis

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

Common set of weights in data envelopment analysis under prospect theory

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