Data envelopment analysis with imprecise data

Despotis, Dimitris K.; Smirlis, Yiannis

doi:10.1016/s0377-2217(01)00200-4

Cited by 327 publications

(208 citation statements)

References 13 publications

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“…However, Wang et al [33] pointed out that Despotis and Smirlis [32] used two different production frontiers to obtain interval efficiency, and thus all DMUs cannot be compared on the basis of a common evaluation criterion. In order to calculate the lower and upper bound of the efficiency of DMU d , Wang et al [33] proposed two linear formulations to generate the bounded interval [E l dd , E u dd ], as follows:…”

Section: Interval Dea Modelsmentioning

confidence: 99%

“…However, Lee et al [31] argued that IDEA model was complicated, and may lead to a rapid increase in computation burden. To solve this problem, Despotis and Smirlis [32] proposed two improved models. Through these two improved models, the lower and upper efficiency of each DMU could be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…Through these two improved models, the lower and upper efficiency of each DMU could be obtained. Wang et al [33] pointed out that Despotis and Smirlis's model [32] used two different production frontiers to measure the efficiencies of DMUs, and this may lead to the efficiencies of DMUs' lack of comparability. To deal with such an issue, Wang et al [33] proposed the new DEA models based on a common frontier to obtain the interval efficiency of each DMU and a minimax regret-based approach was then used for ranking the interval efficiencies of all DMUs.…”

Section: Introductionmentioning

confidence: 99%

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Entropy Cross-Efficiency Model for Decision Making Units with Interval Data

Wang

Hong

2016

Entropy

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Abstract:The cross-efficiency method, as a Data Envelopment Analysis (DEA) extension, calculates the cross efficiency of each decision making unit (DMU) using the weights of all decision making units (DMUs). The major advantage of the cross-efficiency method is that it can provide a complete ranking for all DMUs. In addition, the cross-efficiency method could eliminate unrealistic weight results. However, the existing cross-efficiency methods only evaluate the relative efficiencies of a set of DMUs with exact values of inputs and outputs. If the input or output data of DMUs are imprecise, such as the interval data, the existing methods fail to assess the efficiencies of these DMUs. To address this issue, we propose the introduction of Shannon entropy into the cross-efficiency method. In the proposed model, intervals of all cross-efficiency values are firstly obtained by the interval cross-efficiency method. Then, a distance entropy model is proposed to obtain the weights of interval efficiency. Finally, all alternatives are ranked by their relative Euclidean distance from the positive solution.

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Section: Interval Dea Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Entropy Cross-Efficiency Model for Decision Making Units with Interval Data

Wang

Hong

2016

Entropy

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“…Tongzon (2001) performed efficiency measurements of selected international airports using DEA, and found the ports of Melbourne, Rotterdam, Yokohama and Osaka to be the most inefficient international airports. Despotis & Smirlis (2002) developed an alternative numerical approach for dealing with imprecise data in DEA and so formulated another post-DEA model. Zheng et al (2003) investigated the productivity performance of 600 state enterprises from 1980 to 1994 by using DEA and a Malmquist index, and the empirical results revealed the low average technical efficiency of these firms.…”

Section: Literature Reviewmentioning

confidence: 99%

Relative Efficiency Measurement of Enterprises Operating in the Oltu Stone Industry Using Data Envelopment Analysis

Çelik

Çakmak

Kocacan³

2013

IJBM

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The city of Erzurum is emerging as one of the most popular jewelry centers in Turkey, principally due to its possession of mines for precious stones, and in particular for Oltu stone. Oltu stone is paramount to the international image of the city, and its contribution to the local and national economy is significant. Although recent studies have underscored the enormous potential of the Oltu stone industry, it is encumbent on the enterprises concerned to use their resources effectively and efficiently in order to reduce their costs, and they are also advised to improve service quality and thus satisfy customer needs more closely. With these aims in mind, Data Envelopment Analysis provides a convenient statistical technique that enables the analyst to measure the relative efficiencies of enterprises. This linear-programming-based technique can be used to identify suggestions for improvement with the goal of achieving greater efficiency. This paper uses Data Envelopment Analysis to perform a relative efficiency analysis of enterprises operating in the Oltu stone industry and presents alternatives whereby inefficient enterprises may become more efficient. The paper makes use of output-oriented CCR and BCC models to specify the scenario by calculating three different efficiency scores.

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“…Recently, Cooper et al [4] [14], Entani et al [8], Zhu [16,17], Despotis and Smirlis [71 and Jahanshahloo et al [9,10] [7]. …”

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confidence: 99%