Inverse data envelopment analysis without convexity: double frontiers

Asadi, Farzaneh; Kordrostami, Sohrab; Amirteimoori, Alireza; Bazrafshan, Morteza

doi:10.1007/s10203-022-00377-8

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…A comprehensive inverse nonradial DEA model was, moreover, suggested by Zhang and Cui (2020). Asadi et al (2022) introduced inverse DEA models, but did not include the convexity property. Lu et al (2022) presented an inverse DEA approach to consider the yearly realization way of China’s CO 2 emanation diminishment and financial development purposes from 2020 to 2030.…”

Section: Literature Reviewmentioning

confidence: 99%

Inverse data envelopment analysis optimization approaches with flexible measures

Noveiri

Kordrostami

Ghiyasi

2023

JM2

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Purpose The purpose of this study is to estimate inputs (outputs) and flexible measures when outputs (inputs) are changed provided that the relative efficiency values remain without change. Design/methodology/approach A novel inverse data envelopment analysis (DEA) approach with flexible measures is proposed in this research to assess inputs (outputs) and flexible measures when outputs (inputs) are perturbed on condition that the relative efficiency scores remain unchanged. Furthermore, flexible inverse DEA approaches proposed in this study are used for a numerical example from the literature and an application of Iranian banking industry to clarify and validate them. Findings The findings show that including flexible measures into the investigation effects on the changes of performance measures estimated and leads to more reasonable achievements. Originality/value The traditional inverse DEA models usually investigate the changes of some determinate input-output factors for the changes of other given input-output indicators assuming that the efficiency values are preserved. However, there are situations that the changes of performance measures should be tackled while some measures, called flexible measures, can play either input or output roles. Accordingly, inverse DEA optimization models with flexible measures are rendered in this paper to address these issues.

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Section: Literature Reviewmentioning

confidence: 99%

Inverse data envelopment analysis optimization approaches with flexible measures

Noveiri

Kordrostami

Ghiyasi

2023

JM2

Self Cite

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“…Hassanzadeh et al [14] evaluated the sustainability of countries using their inverse DEA models, including input-oriented and output-oriented inverse semi-oriented radial measures [21,3]. Asadi et al [1] proposed inverse FDH models from two perspectives: optimistic and pessimistic. The performance measures were also addressed in some inverse DEA studies [19], presenting non-discretionary measures.…”

Section: Introductionmentioning

confidence: 99%

An inverse multi-period FDH model with undesirable outputs

Asadi,

Kordrostami,

Amirteimoori

et al. 2023

Croat. oper. res. rev. (Online)

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In some examinations, the changes in performance measures and the efficiency of entities should be addressed over a span of time despite the undesirable outputs and unsatisfactory convexity property. In this study, the performance of processes is analyzed over several periods of time by presenting a multi-period model based on the free disposal hull (FDH) approach, incorporating undesirable outputs. Using the product of availability, performance (efficiency) and quality, the overall equipment effectiveness (OEE) is calculated which shows the level of productivity. After estimating the performance and OEE, an inverse multi-period FDH model is introduced to measure the input changes for the changes made to outputs in different time periods. The proposed approaches are used in the automotive industry to analyze the performance and changes in input measures over multiple periods of time. The results show that the proposed technique is reasonable for assessing the multi-period efficiency, the period and overall OEE of the systems, and the input changes related to several periods while the convexity assumption is violated.

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