Finding closest target for bank branches in the presence of weight restrictions using data envelopment analysis

Razipour-GhalehJough, Somayeh; Lotfı, Farhad Hosseinzadeh; Jahanshahloo, G. R.; Rostamy-Malkhalifeh, Mohsen; Sharafi, Hamid

doi:10.1007/s10479-019-03166-6

Cited by 25 publications

(7 citation statements)

References 54 publications

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“…DEA is one of Operations Research (OR) recognized techniques and is applied to measuring efficiency of a number of peer decision making units (DMUs) (Razipour-GhalehJough et al, 2020;Sorkhi & Paradi, 2020;Xin et al, 2022). DEA uses inputs and outputs ratios for evaluating efficiency of DMUs.…”

Section: Dea and Network Deamentioning

confidence: 99%

RETRACTED ARTICLE: Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

et al. 2022

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In era of reglobalization, sustainably resilient supply chains (SCs) are imperative in corporations to improve performance and meet stockholders’ expectations. However, sustainably resilient SCs could not be effective if are not assessed by using advanced frameworks, systems, and models. As such, developing a novel network data envelopment model (DEA) to appraise sustainably resilient SCs is our purpose in this article. To do so, we present a new double-frontier methodology to provide optimistic and pessimistic efficiency measures in network structures. Moreover, ideas of outputs weak disposability, chance-constrained programming, and discrete dominance are incorporated in a unified framework of modelling efficient and inefficient production technologies. The new network DEA model also can address dissimilar types of data, including undesirable and integer-valued and ratio outputs, stochastic intermediate products, and integer-valued inputs in a unified framework. Furthermore, an aggregated Farrell type efficiency measure is developed which allows to provide the complete ranking of units so that each decision-making unit (DMU) has its own rank in both overall and divisional point of view. We show the unique features of our developed model using a real case study in paint industry to evaluate the efficiency and reducing carbon dioxide (CO2) emissions. The results show that how well the proposed models can evaluate the sustainability and resilience of supply chains in the presence of uncertainty and with dissimilar types of data.

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Section: Dea and Network Deamentioning

confidence: 99%

RETRACTED ARTICLE: Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

et al. 2022

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“…However, Thanassoulis and Dyson (1992) and Zhu (1996) develop models that accommodate preference structures through weights on changes to input-output levels. Adding weight restrictions to the models is actually a way of incorporating value judgements: Ramón et al (2016) and Razipour-GhalehJough et al (2020) deal with the closest targets in DEA models having weight restrictions. Preference information has also been incorporated following interactive approaches, which do not necessarily require prior judgments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(2016) and Razipour‐GhalehJough et al. (2020) deal with the closest targets in DEA models having weight restrictions. Preference information has also been incorporated following interactive approaches, which do not necessarily require prior judgments.…”

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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“…Wu et al [19] incorporated the closest targets technique into carbon emissions abatement allocation and applied it for carbon emissions abatement target setting and allocation for 20 APEC economies. Razipour-GhalehJough et al [51] proposed a closest targets model in the presence of weight restrictions to evaluate and improve the efficiency of Iranian banks.…”

Section: Closest Targets Approach In Deamentioning

confidence: 99%

Stepwise Improvement for Environmental Performance of Transportation Industry in China: A DEA Approach Based on Closest Targets

Huang

2021

Mathematical Problems in Engineering

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Transportation is regarded as an industry with high energy consumption and high CO2 emissions. Little attention has been paid to the environmental performance improvement of China’s transportation industry, especially in a stepwise improvement way. In this study, we first apply the closest targets DEA method to evaluate the environmental performance in the transportation industry of 30 provincial-level regions in China’s mainland from 2010 to 2017. Then, we incorporate the closest targets and context-dependent DEA model and thus conform a stepwise projection path for each inefficient province to improve environmental performance with less effort by the way of identifying a sequence of intermediate closest targets. The empirical study shows that the environmental performance of the transportation industry obtained from the closest targets model is greater than that obtained from the SBM model for each province. Among the three areas, the eastern area performs the best in environmental performance followed by the central region and western region. Shanghai has the best environmental performance. Additionally, compared with conventional DEA models, the proposed stepwise improvement method can generate easier and closer achieved targets for the inefficient provinces. Hainan, Yunnan, and Xinjiang provinces have the lowest environmental performance, which need four steps to achieve efficiency.

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Finding closest target for bank branches in the presence of weight restrictions using data envelopment analysis

Cited by 25 publications

References 54 publications

RETRACTED ARTICLE: Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

RETRACTED ARTICLE: Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

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

Stepwise Improvement for Environmental Performance of Transportation Industry in China: A DEA Approach Based on Closest Targets

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