Finding weak defining hyperplanes of PPS of the BCC model

Jahanshahloo, G. R.; Lotfı, Farhad Hosseinzadeh; Akbarian, Dariush

doi:10.1016/j.apm.2010.02.023

Cited by 27 publications

(11 citation statements)

References 9 publications

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“…Another approach to identifying the equations of the weak de ning hyperplanes of the PPS of the BCC model was proposed by Jahanshahloo et al [38]. The summary of this identi cation is as follows:…”

Section: Finding E Cient Hyperplanesmentioning

confidence: 99%

“…A method for generating all the e cient surfaces of the BCC model passing through DMU p was suggested by [20,38] and improved by [22], which are as follows: …”

Section: Finding E Cient Hyperplanesmentioning

confidence: 99%

“…On the ip side, the simplistic claim that the region of e ciency improvement is more relevant than nding their e ciency is really misleading. Applying the results of [16,[20][21][22]27,38], the current paper aims to obtain the improved region of e ciency by using all the de ning hyperplanes of PPS. Now, there is a need to evaluate the use of DEA as a decision aid for considering the improved region of e ciency based on the overall value judgment; this is clari ed in [39].…”

Section: Introductionmentioning

confidence: 99%

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Finding an improved region of efficiency via DEA-efficient hyperplanes

et al. 2017

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Abstract. The analysis of e ciency is conducted for two vital purposes: rstly, in order to evaluate the current level of e ciency; secondly, to provide information on how to improve the level of e ciency, which is to provide benchmarking information. The ine cient Decision Making Units (DMUs) are usually able to improve their performance, and Data Envelopment Analysis (DEA) projections provide a prescription for improvement. However, sometimes, an ine cient DMU cannot move its performance toward the best practice by either decreasing its inputs or increasing its outputs. On the other hand, it can scarcely reach its e cient benchmark. This research suggests a method to nd an improved region of e ciency through DEA-e cient hyperplanes by providing an algorithm for detecting an improved e ciency path. In addition to the production of reasonable benchmarking information, the proposed algorithm provides the general requirements that satisfy the demands which every professional decision-maker should meet. Finally, we provide a more detailed description of some new issues, extending the insights from this analysis of the benchmark region from the under-evaluated ine cient DMU. Finally, numerical examples are provided to demonstrate the results of the analysis.

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Section: Finding E Cient Hyperplanesmentioning

confidence: 99%

“…A method for generating all the e cient surfaces of the BCC model passing through DMU p was suggested by [20,38] and improved by [22], which are as follows: …”

Section: Finding E Cient Hyperplanesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finding an improved region of efficiency via DEA-efficient hyperplanes

et al. 2017

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“…(7)), if for some (or ), > 1 (or < 1) or if for some l(or q), model (6) (or model (7)) is infeasible then, strong efficient lies on the extreme ray (edge) of PPS and vice versa. (For more details on models (6) and (7) see [12].) We call all efficient DMUs lying on extreme ray (edge) of PPS of CCR model as extreme efficient DMUs, hereafter.…”

Section: Introductionmentioning

confidence: 99%

“…The following super-efficiency models are used for ranking extreme efficient DMUs [12]: 1 is is added to (6) and (7) (see [13]). Remark 1: In (6) and (7), for each and , = = 1 if and only if strong efficient lies on the strong defining hyperplane of PPS.…”

Section: Introductionmentioning

confidence: 99%

A Ranking Method of Extreme Efficient DMUs Using Super-Efficiency Model

Akbarian¹

2013

JAMP

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A multiobjective optimization approach to compute the efficient frontier in data envelopment analysis

Ehrgott

Hasannasab

Raith

2019

Multi Criteria Decision Anal

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Data envelopment analysis is a linear programming‐based operations research technique for performance measurement of decision‐making units. In this paper, we investigate data envelopment analysis from a multiobjective point of view to compute both the efficient extreme points and the efficient facets of the technology set simultaneously. We introduce a dual multiobjective linear programming formulation of data envelopment analysis in terms of input and output prices and propose a procedure based on objective space algorithms for multiobjective linear programmes to compute the efficient frontier. We show that using our algorithm, the efficient extreme points and facets of the technology set can be computed without solving any optimization problems. We conduct computational experiments to demonstrate that the algorithm can compute the efficient frontier within seconds to a few minutes of computation time for real‐world data envelopment analysis instances. For large‐scale artificial data sets, our algorithm is faster than computing the efficiency scores of all decision‐making units via linear programming.

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Finding weak defining hyperplanes of PPS of the BCC model

Cited by 27 publications

References 9 publications

Finding an improved region of efficiency via DEA-efficient hyperplanes

Finding an improved region of efficiency via DEA-efficient hyperplanes

A Ranking Method of Extreme Efficient DMUs Using Super-Efficiency Model

A multiobjective optimization approach to compute the efficient frontier in data envelopment analysis

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