Approaches to determining the relative importance weights for cross-efficiency aggregation in data envelopment analysis

Wang, Ying‐Ming; Wang, Shuaian

doi:10.1057/jors.2012.43

Cited by 47 publications

(29 citation statements)

References 27 publications

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“…Due to the simple aggregation method that ignores the relative importance of the cross-efficiency scores, it is often difficult for the average cross-efficiency approach to reveal all the DMUs' real performance [27].…”

Section: Cross Efficiency Evaluationmentioning

confidence: 99%

A Management and Environmental Performance Evaluation of China’s Family Farms Using an Ultimate Comprehensive Cross-Efficiency Model (UCCE)

et al. 2018

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Family farm emerged as a new form of agricultural production organization in China in recent years. For the purpose of sustainable development, decision-makers, such as farm owners and policy makers, require the precise information of a family farm's state of operation to adopt measures for management improvement and agricultural contamination reduction. Considering this, we established two evaluation systems for the measurement of family farms' management and environmental performance. As demonstrated in several recent studies, data envelopment analysis (DEA) cross efficiency is a useful approach for evaluating and comparing the performance of decision-making units (DMUs). Regarding family farms' performance evaluation issues, we modified the traditional average cross-efficiency method to be the ultimate comprehensive cross-efficiency approach with the integration of two statistical quantities based on the full consideration of family farms' unique features, such as vulnerability and seasonality, resulting from the influence of natural and social factors. Our proposed approach presents more excellent characteristics compared with CCR efficiency and average cross efficiency. Several conclusions regarding the operation of China's family farms are drawn: (i) there is weak positive correlation between family farms' management and environmental performance; (ii) there is an increasing trend for both management and environmental efficiency, along with the augmentation of the utilized agricultural area of family farms, and management performance is therefore more significant; (iii) demand for timely technological instruction to improve family farms' management efficiency is expressed by farm owners who are willing to expand; (iv) to improve family farms' environmental performance, several measures-such as introducing biotechnology, providing subsidies, and environmental education for farmers-should be adopted.

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Section: Cross Efficiency Evaluationmentioning

confidence: 99%

A Management and Environmental Performance Evaluation of China’s Family Farms Using an Ultimate Comprehensive Cross-Efficiency Model (UCCE)

et al. 2018

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“…In the paper by Ruiz et al, the aggregation weights are imposed to reflect the disequilibrium in the structure of the DEA‐weighting schemes used to calculate the CE scores. Wang and Wang proposed three alternative models based on weighted least‐square measures of (1) dissimilarities between the CE scores provided by different DMUs; (2) deviations of CE scores from related self‐efficiency scores; and (3) a combination of these two metrics. Yang et al adopted an evidential reasoning approach to generate aggregation weights that reflect the DM's preference through a transformation of the CE matrix to pieces of evidence.…”

Section: Introductionmentioning

confidence: 99%

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

Oukil

2018

Int J Intell Syst

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Cross‐efficiency (CE) evaluation is an extension of data envelopment analysis (DEA) used for fully ranking decision‐making units (DMUs). The ranking process is normally performed on the matrix of CE scores. An ultimate efficiency score is computed for each DMU through an adequate amalgamation process. The preference ranking approach can be seen as an amalgamation technique based on the rank orders of the CE scores. In this paper, we review this approach by putting more emphasis on the aggregation aspect. We highlight the zero vote issue and we show that the latter has been neglected in the extant aggregation procedures. Consequently, we develop two ordered weighted averaging (OWA)‐based procedures that attempt to meet effectively the requirements of an aggregation mechanism while exploiting the positive properties of the preference‐ranking approach. The merits of the proposed procedures are evaluated on a sample of manufacturing systems by considering, for OWA weights generation, different OWA models with different orness degrees.

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“…When they are simply averaged together, the weight assigned to the self‐evaluated efficiency is only 1/ n if there are n DMUs to be evaluated, whereas the remaining weights, ( n − 1)/ n , are given to those peer‐evaluated efficiencies. The average cross‐efficiency scores sometimes fail to reflect the real performance of all DMUs, since this method simply aggregates them equally by ignoring their relative importance (Wang and Wang, ).…”

Section: Introductionmentioning

confidence: 99%

An improvement in DEA cross‐efficiency aggregation based on the Shannon entropy

Song

Liu

2016

Int Trans Operational Res

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This paper improves a recently proposed Data Envelopment Analysis (DEA) cross‐efficiency aggregation method based on the Shannon entropy. The weights for determining cross‐efficiency are derived from minimizing the square distance of weighted cross‐efficiency and weighted CCR efficiency. Our calculation example indicates that this method may produce inappropriate weights, which is significantly inconsistent with a widely accepted viewpoint. A variance coefficient method based on the Shannon entropy is presented to overcome the drawbacks of the DEA cross‐efficiency aggregation method. In this study, comparisons of weights and cross‐efficiency scores are provided.

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Approaches to determining the relative importance weights for cross-efficiency aggregation in data envelopment analysis

Cited by 47 publications

References 27 publications

A Management and Environmental Performance Evaluation of China’s Family Farms Using an Ultimate Comprehensive Cross-Efficiency Model (UCCE)

A Management and Environmental Performance Evaluation of China’s Family Farms Using an Ultimate Comprehensive Cross-Efficiency Model (UCCE)

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

An improvement in DEA cross‐efficiency aggregation based on the Shannon entropy

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