scholarly journals Recommending investment opportunities given congestion by adaptive network data envelopment analysis model: Assessing sustainability of supply chains

2019 ◽  
Author(s):  
Hossein Hajaji ◽  
Sara Yousefi ◽  
Reza Farzipoor Saen ◽  
Amir Hassanzadeh
Keyword(s):  
Data Envelopment Analysis ◽  
Supply Chains ◽  
Data Envelopment Analysis Model ◽  
Network Data ◽  
Data Envelopment ◽  
Analysis Model ◽  
Adaptive Network ◽  
Dea Model ◽  
Network Data Envelopment Analysis ◽  
Desirable And Undesirable Outputs

Nowadays, forward-thinking companies move beyond conventional structures of organizations and consider all parties of the supply chain. The objective of this paper is to present an adaptive network data envelopment analysis (DEA) model to evaluate overall and divisional efficiency of sustainable supply chains in the presence of desirable and undesirable outputs. Our adaptive network DEA model can assess overall and divisional efficiency of supply chains given managerial and natural disposability. Also, it suggests new investment opportunity given congestion type. A case study is presented.

scholarly journals MEASURING EFFICIENCY OF A UNIVERSITY FACULTY USING A HIERARCHICAL NETWORK DATA ENVELOPMENT ANALYSIS MODEL

2018 ◽  
Author(s):  
Rosmaini Kashim ◽  
Maznah Mat Kasim ◽  
Rosshairy Abd Rahman
Keyword(s):  
Data Envelopment Analysis ◽  
Measurement Model ◽  
Data Envelopment Analysis Model ◽  
Network Data ◽  
University Faculty ◽  
Data Envelopment ◽  
Analysis Model ◽  
Dea Model ◽  
Network Data Envelopment Analysis ◽  
The Impact

An efficiency measurement model of a university faculty is proposed with additional new sub-functions that produce new output variables, based on the network Data Envelopment Analysis (DEA) model for systems with a hierarchical structure.For production systems composed of hierarchical processes, the system efficiency is well represented as the aggregated performance of the components involved in the system. It is identified that the conventional DEA model ignores internal process activities in a university. Therefore, an improved DEA model based on a network structure that accounts for more activities in a university is proposed to measure its overall efficiency. The impact of major functions of a university are taken into account to represent the output variables in assessing the efficiency. Currently, collaboration activities have been given more attention, so, this variable is suggested as a new output for the hierarchical production system. In order to show the practicality of the model, a hypothetical set of data of 14 faculties has been used as a numerical example. The results show that none of the faculties is relatively efficient since its functions were found to be inefficient. The proposed model enables to help the management of university faculties to identify weaknesses of each function and thus to plan for suitable actions on improving the overall performance of the university.  

scholarly journals Evaluation of post-graduate programs using a network data envelopment analysis model

DYNA ◽  
2018 ◽  
Vol 85 (204) ◽  
pp. 83-90 ◽  
Author(s):  
Lidia Angulo Meza ◽  
João Carlos Soares de Mello ◽  
Silvio Figueiredo Gomes Junior ◽  
Plácido Moreno
Keyword(s):  
Data Envelopment Analysis ◽  
Graduate Programs ◽  
Data Envelopment Analysis Model ◽  
Network Data ◽  
Data Envelopment ◽  
Analysis Model ◽  
Network Dea ◽  
Post Graduate ◽  
Network Data Envelopment Analysis

A pesar de que los modelos estándar del Análisis Envolvente de Datos (DEA) han sido ampliamente utilizados en la evaluación de la eficiencia en educación, existen pocos estudios que utilizan modelos DEA en red (Network DEA – NDEA) en la evaluación educativa. En el presente trabajo, se ha propuesto una alternativa a la evaluación oficial realizada a cada tres años por la CAPES (agencia brasileña para la regulación de los programas de post-graduación) mediante un modelo DEA en red. El uso de NDEA se justifica ya que dependiendo del punto de vista algunas variables pueden ser consideradas como entradas o como salidas. El uso de NDEA evita la necesidad de decidir si una variable es una entrada o una salida de todo el proceso. Esto ocurre porque una variable puede ser tanto una salida para una etapa y una entrada para otro. Nuestro modelo relacional NDEA evalúa tanto la productividad como la calidad junto con la eficiencia global, a partir de datos bibliométricos.

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