Computing Efficiency for Decision Making Units with Negative and Interval Data

Piri, M.; Givehchi, Sh.; Keshavarz, Mehdi

doi:10.5899/2016/dea-00120

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…About Adjusted DEA, it is a type of DEA for which interesting improvements were envisaged, such as the use of qualitative variables (Sherman & Zhu, 2006) and negative or an interval data (Piri et al, 2016). Thus, Adjusted DEA is appropriate for composite index elaboration, as it deals with data of various types and gives optimal weights for indicators.…”

Section: Discussionmentioning

confidence: 99%

Research Governance in Public University: Proposal of a Composite Index

Boutchich

2021

Journal of Education

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This work aims to elaborate a governance composite index for research laboratories in public university. This index is composed by an indicator of responsible liberalism associating effectiveness and ethics, to which are added an organizational management indicator and a strategic management indicator. To achieve the above aim, several methods are used, such as adjusted data envelopment analysis and geometric mean to aggregate indicators to calculate the composite index, Vigier index to compute responsible liberalism indicator, and tools to measure the validity and the reliability of indicators. The findings show that the developed index can be applied in any context.

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Section: Discussionmentioning

confidence: 99%

Research Governance in Public University: Proposal of a Composite Index

Boutchich

2021

Journal of Education

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“…During the same year, the probability in the DEA program was introduced under the name of Chance-constrained DEA Model (Land et al, 1993) Sherman and Zhu (2006). Since 2007, methods to treat negative and interval data in the DEA are conceived (Piri et al, 2016). In 2012, Lee and Zhu conceive an alternative method to traditional Super-Efficiency Model for ranking efficient units and removing outliers.…”

Section: Deamentioning

confidence: 99%

Relevant Ingredients for Identifying Factors With Significant Impact on Research Structures Efficiency in Higher Education

Boutchich

2020

Journal of Education

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This work proposes relevant ingredients to highlight the factors with significant impact on efficiency of research structures in higher education. The ingredients in question include methods and their implementation taking into account the choice and operationalization of factors as well as options of the retained methods. The methods employed in this work are Data Envelopment Analysis and Multiple Linear Regression. The originality of this work lies in its intermethodology, as efficiency scores which are determined by Data Envelopment Analysis are considered as the dependent variable of the Multiple Linear Regression. Its originality resides also in dealing with the Multiple Linear Regression methods that are little approached in the works which use this technique and in combining parametric and nonparametric methods. This work has social and research implications, as research structures feel that research subsidies they receive depend on their outcomes and leads them to privilege factors with great impact on their efficiency. This work has shown that to determine the factors that have a significant impact on the research structures efficiency, research activities proposed by the international research institutions and contingency factors of the research structures must be taken into account. On the other side, the regression methods used to highlight the aforementioned impact become outmoded and needed some improvements. Finally, for an appropriate measure of research activities, the ideal is to integrate Data Envelopment Analysis and Multiple Linear Regression, especially as linear programming has already been integrated in Multiple Linear Regression.

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“…Emrouznejad and Yang (2016) proposed a performance index based on efficient and anti-efficient frontiers in DEA models without explicit inputs (DEA-WEI) and developed the corresponding performance index in quadratic DEA-WEI models. Piri et al (2016) proposed a method to evaluate the efficiency scores of DMUs with interval data in which the lower and upper bounds of intervals can take both negative and positive values. Amirteimoori et al (2017) suggested an approach to integrate the optimistic and pessimistic perspectives to obtain the interval efficiency scores of units with interval data.…”

Section: Introductionmentioning

confidence: 99%

A complete ranking of decision making units with interval data

Khezri

Jahanshahloo

Dehnokhalaji

et al. 2022

IJOR

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Interval Data Envelopment Analysis (Interval DEA) deals with the problem of efficiency assessment when the inputs and/or outputs of Decision Making Units (DMUs) are given as interval data. This paper focuses on the problem of ranking DMUs with interval data. First, we define extreme efficient units, super efficiency score, the best and the worst efficiency (inefficiency) frontiers in the interval DEA context. Then, we propose a novel method based on the lower and upper super efficiency scores of a unit under evaluation and the distance of that unit to four developed frontiers. Our method ranks all efficient and inefficient units which is one of the main advantages of it. Our method uses several essential criteria simultaneously to rank units with interval data. These criteria increase the discrimination power of our proposed method. Potential application of this method is illustrated with a dataset consisting of 30 branches of the social security insurance organization in Tehran.

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Computing Efficiency for Decision Making Units with Negative and Interval Data

Cited by 4 publications

References 15 publications

Research Governance in Public University: Proposal of a Composite Index

Research Governance in Public University: Proposal of a Composite Index

Relevant Ingredients for Identifying Factors With Significant Impact on Research Structures Efficiency in Higher Education

A complete ranking of decision making units with interval data

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