scholarly journals Applying Data Envelopment Analysis and Clustering Analysis in Enhancing the Performance of Philippine National Police - District VI in the Province of Cavite

2017 ◽  
Vol 7 (2) ◽  
pp. 01-21
Author(s):  
Mengvi P. Gatpandan ◽  
Shaneth C. Ambat
Keyword(s):  
Data Envelopment Analysis ◽  
Clustering Analysis ◽  
Data Envelopment ◽  
National Police

Efficiency of Slovenian Public General Libraries: A Data Envelopment Analysis Approach

2017 ◽  
Vol 15 (3) ◽  
pp. 559-581 ◽  
Author(s):  
Andrej Srakar ◽  
Eva Kodrič-Dačić ◽  
Klemen Koman ◽  
Damjan Kavaš
Keyword(s):  
Data Envelopment Analysis ◽  
Clustering Analysis ◽  
Analysis Approach ◽  
Data Envelopment ◽  
Regression Methods ◽  
System Requirements

In the article, we study financing and efficiency of Slovenian Public General Libraries. We employ a rich dataset of CEZAR – Centre for Development of Libraries for the years 2008-2014 for 58 libraries and use data envelopment analysis and regression methods to study the efficiency of libraries over the years. Our main results show that the problems for the libraries in this period did not lie in the lowered efficiency but more likely in other system requirements. We also provide a grouping of libraries following clustering analysis and show it had significant effects on the performance of the libraries.

scholarly journals Port Efficiency Incorporating Service Measurement Variables by the BiO-MCDEA: Brazilian Case

2019 ◽  
Vol 11 (16) ◽  
pp. 4340 ◽  
Author(s):  
Renata Machado de Andrade ◽  
Suhyung Lee ◽  
Paul Tae-Woo Lee ◽  
Oh Kyoung Kwon ◽  
Hye Min Chung
Keyword(s):  
Data Envelopment Analysis ◽  
Clustering Analysis ◽  
Programming Approach ◽  
Data Envelopment ◽  
Strong Correlations ◽  
Discrimination Power ◽  
Port Efficiency ◽  
Dea Models ◽  
Cargo Throughput ◽  
Brazilian Ports

Data envelopment analysis (DEA) has many advantages for analyzing the efficiency of decision-making units, as well as drawbacks, such as a lack of discrimination power. This study applied bi-objective multiple-criteria data envelopment analysis (BiO-MCDEA), a programming approach used to overcome the limitations of traditional DEA models, to analyze the efficiency of 20 Brazilian ports with a consideration of six input and one output variables from 2010 to 2016. Two time-related variables were included to reflect current problems faced by Brazilian ports experiencing long wait times. The results reveal a significant disparity in port efficiency among Brazilian ports. The top five most efficient ports are those with the highest cargo throughput. A clustering analysis also confirmed a strong correlation between cargo throughput and port efficiency scores. Total time of stay, pier length, and courtyard also had strong correlations with the efficiency scores. The clustering method divided Brazilian ports into three groups: efficient ports, medium efficient ports, and inefficient ports.

Data Envelopment Analysis Theory, Methodology and Applications

1997 ◽  
Vol 48 (3) ◽  
pp. 332-333 ◽  
Author(s):  
A Charnes ◽  
W Cooper ◽  
A Y Lewin ◽  
L M Seiford
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Analysis Theory
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