A Procedure for Ranking Efficient Units in Data Envelopment Analysis

Andersen, Per Kragh; Petersen, Niels Christian

doi:10.1287/mnsc.39.10.1261

Cited by 3,001 publications

(1,526 citation statements)

References 10 publications

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“…The basis of this approach is the estimation of input-as well as output-oriented order-m performance scores. In contrast to the order-m model presented above, Simar (2003) suggests to employ a 'leave-one-out' approach which is equivalent to the 'super-efficiency' concept of Andersen and Petersen (1993): During the estimation the unit of interest is excluded from the reference set. The calculations are conducted for different values of m. A unit is a potential outlier if its super-efficiency input order-m efficiency measures are greater than one plus a certain threshold α (in case of the output orientation, respectively, if the efficiency score is smaller than one minus α).…”

Section: A Methods To Calculate Innovation Performancementioning

confidence: 99%

Measuring Regional Innovativeness - A Methodological Discussion and an Application to One German Industry

Broekel¹,

Brenner

2007

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Tom Broekel * Terms of use: Documents in Thomas BrennerMax Planck Institute of Economics, Jena, Germany Abstract The regional or national innovation performance has been repeatedly measured in the literature; but it has so far not been discussed what this means, especially in relation to a region. What is the contribution of a region to innovation output? The usual approaches implicitly assume that higher innovation outputs per inhabitant, employee, or R&D employee can be assigned to a region. We argue that more insights are gained if we distinguish between various mechanisms that influence the innovation activities in a region. Different analyses need to be conducted, using different variables and including different local factors. Furthermore, we see no justification for using a linear dependence of innovation activity on the number of inhabitants or employees as a benchmark for performance. We use a method that takes into account these arguments and apply it to the Electrics & Electronics industry in Germany.JEL-classification: R11, R15, O31

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Section: A Methods To Calculate Innovation Performancementioning

confidence: 99%

Measuring Regional Innovativeness - A Methodological Discussion and an Application to One German Industry

Broekel¹,

Brenner

2007

SSRN Journal

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“…If several DMUs are recognised as efficient, then the Anderson-Peterson (Andersen and Petersen, 1993) ranking approach is used to distinguish between DMUs.…”

Section: Linear Form Of Input-oriented Crs Modelmentioning

confidence: 99%

Cooperative mechanism based on data envelopment analysis and artificial neural network to measure efficiency: case study of Iranian ports

Mousavizadeh

Khalili‐Damghani

2017

IJADS

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Ports are main parts of business and transportation in every country. So, measuring the efficiency of ports is essential. There are several criteria involved in assessment of ports performance. This paper presents a cooperative approach based on data envelopment analysis (DEA) and artificial neural network (ANN) to measure the efficiency and ranking of 11 main ports in Iran. The results show that the DEA-ANN approach outperforms classic DEA approach. The proposed approach provides following advantages: 1) high discrimination power in presence of low number of decision making units (DMUs) and high number of inputs and outputs; 2) suitable estimation of future production function; 3) providing a proper estimation of future efficiency scores; 4) no need to use super-efficiency models to rank the DMUs. The proposed model of this study can help managers to evaluate the performance of DMUs on the basis of future estimated inputs and outputs.Keywords: data envelopment analysis; DEA; artificial neural network; ANN; hybrid DEA-ANN; efficiency measurement; port efficiency; Iran. He has published more than 80 scientific papers in international journals and conferences. He serves as an Associate Editor of six international scientific journals. His researches can be found at: http://kaveh-khalili.webs.com. His research interests are computational intelligence, soft computing, data envelopment analysis, fuzzy optimisation and decision-making, applied operations research.

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“…Therefore, for each individual DMU a new distance result is obtained, which leads to a new ranking, or even a rating of all the original efficient DMUs. Anderson and Petersen (1993) have developed the Super-Efficiency model based on a radial projection (including a CCR model) to arrive at a ranking of all efficient DMUs. The efficiency scores from a superefficiency model are thus obtained by eliminating the data on the DMU o to be evaluated from the solution set.…”

Section: A2 Super-efficiency Modelmentioning

confidence: 99%

A Target-Oriented Data Envelopment Analysis for Energy-Environment Efficiency Improvement in Japan

2013

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A Procedure for Ranking Efficient Units in Data Envelopment Analysis

Cited by 3,001 publications

References 10 publications

Measuring Regional Innovativeness - A Methodological Discussion and an Application to One German Industry

Measuring Regional Innovativeness - A Methodological Discussion and an Application to One German Industry

Cooperative mechanism based on data envelopment analysis and artificial neural network to measure efficiency: case study of Iranian ports

A Target-Oriented Data Envelopment Analysis for Energy-Environment Efficiency Improvement in Japan

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