Sensitivity and stability analysis in DEA

Jahanshahloo, G. R.; Hosseinzadeh, F.; Shoja, N.; Sanei, Masoud; Tohidi, Ghasem

doi:10.1016/j.amc.2004.09.092

Cited by 30 publications

(20 citation statements)

References 10 publications

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“…To conduct sensitivity analysis of each e cient unit called DMU p , Jahanshahloo et al [21] used de ning hyperplanes that are binding at DMU p and the new frontier, which is formed by the omission DMU p from the observations set. Moreover, the MOLP problem suggested by [16] for nding e cient hyperplanes with the VRS assumption of technology in DEA is as follows:…”

Section: Finding E Cient Hyperplanesmentioning

confidence: 99%

“…Many more papers have studied sensitivity analysis with various data types such as integer data type [3], fuzzy data [19], etc. Another model proposed by Huang et al [20], later generalized by Jahanshahloo et al [21], showed that all the e cient surfaces of the BCC model passing through DMU P were generated for the case; in this model, the largest stability region of the under-evaluated extreme e cient DMU was constructed by the omission of DMU P from the observations set. Later, the largest stability region proposed in their paper was improved by [22].…”

Section: Introductionmentioning

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%

Section: Introductionmentioning

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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“…An important feature of this DEA technique which has been studied by many researchers is sensitivity analysis. During the recent years, the issue of sensitivity and stability of DEA results has been extensively studied (see for example Jahanshahloo et al 2004Jahanshahloo et al , 2005a. The topic of sensitivity (=stability or robustness) analysis has taken a variety of forms in the DEA literature.…”

Section: Introductionmentioning

confidence: 99%

Finding stability regions for preserving efficiency classification of variable returns to scale technology in data envelopment analysis

Zamani

Borzouei

2016

J Ind Eng Int

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This paper addresses issue of sensitivity of efficiency classification of variable returns to scale (VRS) technology for enhancing the credibility of data envelopment analysis (DEA) results in practical applications when an additional decision making unit (DMU) needs to be added to the set being considered. It also develops a structured approach to assisting practitioners in making an appropriate selection of variation range for inputs and outputs of additional DMU so that this DMU be efficient and the efficiency classification of VRS technology remains unchanged. This stability region is simply specified by the concept of defining hyperplanes of production possibility set of VRS technology and the corresponding halfspaces. Furthermore, this study determines a stability region for the additional DMU within which, in addition to efficiency classification, the efficiency score of a specific inefficient DMU is preserved and also using a simulation method, a region in which some specific efficient DMUs become inefficient is provided.

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“…One of the problems in DEA is to find the equations of these defining hyperplanes. There are many researches undertaken on the subject of finding strong defining hyperplanes (see for example Amirteimoori et al [1], Amirteimoori et al [2], M. Davtalab-Olyaie et al [7], Jahanshahloo et al [11], Jahanshahloo et al [10], Yu et al [16], Wei et al [15], Hosseinzadeh et al [9] and Olesen et al [14]). However, less attention has been paid about finding weak defining hyperplanes of the PPS of the CCR model (see Wei et al [15]).…”

Section: Introductionmentioning

confidence: 99%

On Characterizing weak defining hyperplanes (weak Facets) in DEA with Constant Returns to Scale Technology

Akbarian¹

2017

Data Envelopment Analysis and Decision Science

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The Production Possibility Set (PPS) is defined as a set of inputs and outputs of a system in which inputs can produce outputs. The Production Possibility Set of the Data Envelopment Analysis (DEA) model is contain of two types defining hyperplanes (facets); strong and weak efficient facets. In this paper, the problem of finding weak defining hyperplanes of the PPS of the CCR model is dealt with. However, the equation of strong defining hyperplanes of the PPS of the CCR model can be found in this paper. We state and prove some properties relative to our method. To illustrate the applicability of the proposed model, some numerical examples are finally provided. Our algorithm can easily be implemented using existing packages for operation research, such as GAMS.

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Sensitivity and stability analysis in DEA

Cited by 30 publications

References 10 publications

Finding an improved region of efficiency via DEA-efficient hyperplanes

Finding an improved region of efficiency via DEA-efficient hyperplanes

Finding stability regions for preserving efficiency classification of variable returns to scale technology in data envelopment analysis

On Characterizing weak defining hyperplanes (weak Facets) in DEA with Constant Returns to Scale Technology

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