DEA for nonhomogeneous mixed networks

Barat, Mona; Tohidi, Ghasem; Sanei, Masoud

doi:10.1016/j.apmrv.2018.02.003

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…Traditional DEA models assume that all DMUs shared homogeneous technology, however, this assumption ignores heterogeneous technology derived from different external environments, such as systematic, cultural, regulatory and endowment differences. Recently, non-homogeneous networks have been considered in DEA by some scholars [23,24]. In line with previous studies, we incorporated technological heterogeneity into the DEA model rather than time heterogeneity.…”

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confidence: 94%

The Sustainability Performance of Chinese Banks: A New Network Data Envelopment Analysis Approach and Panel Regression

Huang

Shao

2019

Sustainability

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This paper develops a new network data envelopment analysis (DEA) model that simultaneously integrates the non-convex metafrontier and undesirable outputs and which is super efficient at performing dynamic network slacks-based measures. The model is employed to discuss the efficiency of 36 commercial banks in China during the years 2010–2014. The efficiency of these banks shows significant heterogeneity and the efficiency of most foreign banks has much room for improvement. Regarding both the non-convex metafrontier and the group frontier, state-owned banks perform the best, followed by joint-stock banks, with foreign banks performing the worst; the same is true for the technology gap ratios. The empirical results produced by the feasible generalized least squares estimation method indicate that liquidity and scale effects exert positive impacts on bank efficiency. An alternative estimation method confirmed that the conclusions were robust.

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confidence: 94%

The Sustainability Performance of Chinese Banks: A New Network Data Envelopment Analysis Approach and Panel Regression

Huang

Shao

2019

Sustainability

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“…Efficiency analysis in this kind of models is of high importance. Du et al (2015) and Barat et al (2018) focused on heterogeneity in network DEA models. The paper Singh and Ranjan (2017) is focused on efficiency analysis of non-homogeneous parallel systems for the performance measurement in higher education which is an important topic not only in our conditions.…”

Section: Methodsmentioning

confidence: 99%

Data Envelopment Analysis Models in Non-Homogeneous Environment

Jablonský¹

2019

Acta Univ. Agric. Silvic. Mendelianae Brun.

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Data envelopment analysis (DEA) is a non-parametric method that is widely used for relative efficiency and performance evaluation of the set of decision-making units (DMUs). It is based on maximization of a weighted sum of outputs produced by the unit under evaluation divided by the weighted sum of inputs of the same unit, and the assumption that this ratio for all other units has to be lower or equal to 1. An important assumption for applications of DEA models is the homogeneity of the units. Unfortunately, the homogeneity assumption is not fulfilled in many real applications. The paper deals with the analysis of efficiency using DEA models in the non-homogeneous environment. One of the problems lies in non-homogeneous outputs. In this case, the units under evaluation spend the same inputs but produce completely or at least partly different set of outputs. The paper formulates several models how to deal with this problem and compares the results on a numerical example. Other main sources of non-homogeneity are discussed as an excellent possible starting point for future research.

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“…However, multi-objective optimization requires the definition of weights, which in the context of product design, biases towards local optima metrics (Tavana et al (2016)). To avoid biased weights on evaluation metrics, Data Envelopment Analysis (DEA) (Charnes et al (1978)) is a well-known nonparametric approach which computes weights based on the fact that each evaluation item gets the highest evaluation in relative terms; thus, it is possible to construct a one-dimensional indicator avoiding biases to local optima judgements (Barat et al (2018); Forsund (2018)), which has positive implications for marketing and product development. There exists several studies focusing on DEA, for instance, the work by (Doyle (2014)) and (Seiford and Zhu (2003)) on printers, and the work by (Papagapiou et al (1997)) on vehicles.…”

Section: Introductionmentioning

confidence: 99%

On Vehicle Evaluation and Design Using Data Envelopment Analysis with Hierarchical Concepts

Parque¹,

Honobe²,

Miura³

et al. 2019

Proc. Int. Conf. Eng. Des.

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In recent years, product complexity in terms of function and structure has been driven by technological development in complementary components. Designing unbiased product evaluation metrics being to grasp the complex relationships of product features, and able to capitalize on market needs has become a challenge in industrial practice.In this paper, we propose a hybrid framework in which evaluation models are generated by integrating Interpretive Structural Modeling (ISM), Hierarchical Clustering and Data Envelopment Analysis (DEA). Whereas ISM constructs hierarchical digraphs (skeletons), Hierarchical Clustering reduces dimensionality of pairwise comparisons (correlations) of design variables, and suggests possible evaluation configurations, and DEA computes weights to provide optimal evaluation metrics. Our computational experiments using more than twenty thousand vehicles from 1982 to 2013 confirmed the feasibility and usefulness of DEA with hierarchical concepts to generate the optimal vehicle evaluation metric, and to suggest configurations for vehicle design layouts.

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DEA for nonhomogeneous mixed networks

Cited by 16 publications

References 17 publications

The Sustainability Performance of Chinese Banks: A New Network Data Envelopment Analysis Approach and Panel Regression

The Sustainability Performance of Chinese Banks: A New Network Data Envelopment Analysis Approach and Panel Regression

Data Envelopment Analysis Models in Non-Homogeneous Environment

On Vehicle Evaluation and Design Using Data Envelopment Analysis with Hierarchical Concepts

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