DEA target setting approach within the cross efficiency framework

Chen, Lei; Wang, Ying‐Ming

doi:10.1016/j.omega.2019.05.008

Cited by 41 publications

(19 citation statements)

References 23 publications

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“…(2020), and Van Puyenbroeck et al. (2021), who use composite indicators, Chen and Wang (2019), who propose a target setting approach within the framework of cross efficiency, Lim (2018), who deal with forecasting targets in presence of infeasibility, Moreno and Lozano (2018), who combine DEA and network DEA, Wu et al. (2020), who combine DEA and game theory, An et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying suitable benchmarks in the way toward achieving targets using data envelopment analysis

Ruiz

Sirvent

2021

Int Trans Operational Res

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It was earlier claimed by the authors that the data envelopment analysis (DEA) models commonly used only implement objectives concerned with target setting, while peer identification, which is a key issue for the benchmarking, is actually a by‐product. For this reason, they developed some bi‐objective DEA benchmarking models that pay special attention to the selection of benchmarks among peers, in addition to setting the closest targets. Following an analogous approach, we here continue that research and propose new models that provide a response to the question of whom to emulate to achieve the targets that have been set for a given organization. Thus, the proposed approach now aims to relate the setting of targets and the identification of peers, specifically identifying peers that may serve as benchmarks for improving performance in the direction established by the targets set. As a result, the models identify the so‐called leading benchmark, which is the one whose performance is most aligned with the targets. Eventually, a series of targets and peers are generated by varying the importance attached to the two objectives considered, which offer decision‐makers alternatives for the selection of a course of action when planning improvements.

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Section: Literature Reviewmentioning

confidence: 99%

Identifying suitable benchmarks in the way toward achieving targets using data envelopment analysis

Ruiz

Sirvent

2021

Int Trans Operational Res

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“…The traditional DEA model ignores the cooperation relationship among DMUs, so it is difficult to evaluate the DMUs' efficiency reasonably (Chen et al, 2017). The traditional self-evaluation DEA method usually exaggerates the effects of several inputs or outputs of the evaluated DMU, resulting in unrealistic results (Chen et al, 2020). Doyle and Green (1994) explained cross-efficiency models in a very comprehensive way including their meanings, uses, and derivations.…”

Section: Previous Related Research Workmentioning

confidence: 99%

“…Various applications of these models are found in literature such as Wu et al (2009), Lu et al (2007) among others. Chen et al (2020) proposed a DEA target-setting approach within the cross-efficiency framework via some models and formulations. Mirmozaffari et al (2017) ranked heart hospitals using crossefficiency and two-stage DEA.…”

Section: Previous Related Research Workmentioning

confidence: 99%

Understanding Self and Cross-Efficiencies and Their Relations in Data Envelopment Analysis

Gupta¹,

Rajeshwari²

2021

JSR

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This paper explicitly talks about the relationship between self-efficiency and cross efficiencies. The self-efficiency and cross efficiencies are used in different research works and various secondary goals are also developed using these measurements, but there is no explicit statement or derivation found in any prior art regarding the relationship between self and best cross efficiency for a DMU. Thus, in the present work, the relationship between self and cross efficiencies is examined explicitly and established via some theorems. These relationships can be useful in DEA's further formulations like goal programming models. The paper also complements the author's previous book chapter wherein they have used these relationships. A Numerical illustration is given in the end to establish the results.

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“…(2012), Ruiz (2013), Wu et al. (2016a, 2016b), Chen and Wang (2020), Chen et al. (2020a), Contreras et al.…”

Section: Introductionmentioning

confidence: 99%

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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DEA target setting approach within the cross efficiency framework

Cited by 41 publications

References 23 publications

Identifying suitable benchmarks in the way toward achieving targets using data envelopment analysis

Identifying suitable benchmarks in the way toward achieving targets using data envelopment analysis

Understanding Self and Cross-Efficiencies and Their Relations in Data Envelopment Analysis

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

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