A multi-perspective dynamic network performance efficiency measurement of an evacuation: A dynamic network-DEA approach

Herrera-Restrepo, Oscar; Triantis, Konstantinos; Trainor, Joseph; Murray‐Tuite, Pamela; Edara, Praveen

doi:10.1016/j.omega.2015.04.019

Cited by 32 publications

(12 citation statements)

References 39 publications

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“…() and Andersson et al. () and an evacuation analysis conducted by Herrera‐Restrepo () could not be identified among the studies by Liu et al. ().…”

Section: Evolution Of the Ddea Literaturementioning

confidence: 94%

“…Our literature review allows us to identify the DEA Solver Pro software as the most popular tool used in application papers (Škrinjarić, ; Avkiran, ; Herrera‐Restrepo et al., ). Indeed, DDEA models can also be modeled and solved by different optimization tools, requiring the user to write down the model in the appropriated languages required by the solvers.…”

Section: Evolution Of the Ddea Literaturementioning

confidence: 99%

“…The selection of variables and parameters is a critical issue in several DDEA works found in the literature (Avkiran, 2015;Fukuyama et al, 2016;Herrera-Restrepo et al, 2016;Pointon and Matthews, 2016). Avkiran (2015) pointed out that the familiar rule-of-thumb has a series of shortcomings when employed with DDEA models.…”

Section: Future Research Directionsmentioning

confidence: 99%

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A review of Dynamic Data Envelopment Analysis: state of the art and applications

Mariz

Almeida

Aloise

2017

Int Trans Operational Res

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This article reports the evolution of the literature on Dynamic Data Envelopment Analysis (DDEA) models from 1996 to 2016. Systematic searches in the databases Scopus and Web of Science were performed to outline the state of the art. The results enabled the establishment of DDEA studies as the scope of this article, analyzing the transition elements to represent temporal interdependence. The categorization of these studies enabled the mapping of the evolution of the DDEA literature and identification of the relationships between models. The three most widely adopted studies to conduct DDEA research were classified as structuring models. Mapping elucidated the literature behavior through three phases and showed an increase in publications with applications in recent years. The analysis of applications indicated that most studies address evaluations in the agriculture and farming, banking and energy sectors and consider the facilities as transition elements between analysis periods.

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“…() and Andersson et al. () and an evacuation analysis conducted by Herrera‐Restrepo () could not be identified among the studies by Liu et al. ().…”

Section: Evolution Of the Ddea Literaturementioning

confidence: 94%

Section: Evolution Of the Ddea Literaturementioning

confidence: 99%

See 1 more Smart Citation

A review of Dynamic Data Envelopment Analysis: state of the art and applications

Mariz

Almeida

Aloise

2017

Int Trans Operational Res

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“…Some scholars have used phased DEA to evaluate the energy and transfer efficiencies of transport [30,31]. Furthermore, a network DEA model was used to evaluate the performance level of India's public transport sector and traffic system [32,33]. However, we find that the DEA method as used by the above-mentioned studies still fails to avoid the truncation drawbacks, i.e., they are impossible to accurately measure the efficiency of the effective evaluation unit of DEA and the input-output redundancy cannot be solved at the same time.…”

Section: Introductionmentioning

confidence: 99%

“…However, we find that the DEA method as used by the above-mentioned studies still fails to avoid the truncation drawbacks, i.e., they are impossible to accurately measure the efficiency of the effective evaluation unit of DEA and the input-output redundancy cannot be solved at the same time. The undesired output of the super-slacks-based measurement-data envelopment analysis (US-SBM-DEA) model put forward by Cheng can better solve the problems arising from the above [28][29][30][31][32][33] efficiency measurement methods. Then this model has been widely used in the efficiency evaluation.…”

Section: Introductionmentioning

confidence: 99%

Regional Differences and Spatial Aggregation of Sustainable Transport Efficiency: A Case Study of China

Sun

et al. 2018

Sustainability

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In the past few decades, traffic congestion, traffic accidents, and air pollution caused by transport become increasingly serious in China, so the issue of sustainable development of transport has attracted much attention. This study explores the development level of provincial sustainable transport in China, measures the level of sustainable development of China's transport from the perspective of transport efficiency, and analyzes the differences and spatial effects of sustainable transport development. The undesired output of the super slacks-based-measurement data envelopment analysis (US-SBM-DEA) model is used to measure the sustainable transport efficiency (STE) in different provinces of China, and then the coefficient of variation (CV) and Gini coefficient (GC) are used to explore the regional differences of STE. Finally, we analyze the spatial aggregation of STE by the index of Moran's I. The results show that the regional mean of STE presents a distribution in the order of eastern > western > central > northeastern regions. The CV reveals that there is a local σ-convergence in the STE differences among the four regions during the study period. The Moran scatter plot of STE shows that the provincial STEs in China are mainly the aggregations of high-low and low-low with the latter being more obvious. The GC basically remained at a relatively stable level during 2007-2015 while quickly decreased in 2016. i.e., the equity of sustainable transport increased dramatically in 2016. These results meet the actual development of the sustainable transport in China's provinces and reflect the level of sustainable transport efficiency more objectively. The results of this study provides theoretical support for the provincial governments to formulate efficient transport policies.

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Performance evaluation of Indian banks using feature selection data envelopment analysis: A machine learning perspective

Kumar

Shrivastav

Mukherjee

2021

Journal of Public Affairs

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The early signal of the potential risk of bank failure is imperative for various stakeholders such as management personnel, lenders, and shareholders. This study has developed a new feature selection‐based data envelopment analysis (DEA) model to calculate the efficiencies and predict the stress of Indian banks. The feature selection‐based data envelopment analysis (DEA) model combines feature selection methodology (a machine learning technique), with the traditional data envelopment analysis. The proposed model is able to map the DEA ranks, and the status of both failure and success of banks, and other similar decision‐making units (DMUs). It also helps in solving the problems associated with the selection of appropriate input and output features as well as the time‐dependent data points which usually have a lagged effect. The proposed model is applicable only when appropriate samples of past data of DMUs performance are available, whereby it maps both the input and output features with performance. Importantly, while dealing with the past data for selecting the appropriate inputs and outputs, it is imperative to select all the indicators that affect the performance of DMUs and thereby reduce the number of features using the machine learning approach. The proposed model is one of its kind, integrating a machine learning technique to a nonparametric frontier decision model.

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A multi-perspective dynamic network performance efficiency measurement of an evacuation: A dynamic network-DEA approach

Cited by 32 publications

References 39 publications

A review of Dynamic Data Envelopment Analysis: state of the art and applications

A review of Dynamic Data Envelopment Analysis: state of the art and applications

Regional Differences and Spatial Aggregation of Sustainable Transport Efficiency: A Case Study of China

Performance evaluation of Indian banks using feature selection data envelopment analysis: A machine learning perspective

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