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

Renata Machado de Andrade (), Suhyung Lee (), Paul Tae-Woo Lee (), Oh Kyoung Kwon () and Hye Min Chung ()
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Renata Machado de Andrade: Graduate School of Logistics, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea
Suhyung Lee: Graduate School of Logistics, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea
Paul Tae-Woo Lee: Ocean College, Zhejiang University, No.1, Haida Zheda Road, Zhoushan 316021, China
Oh Kyoung Kwon: Asia Pacific School of Logistics, Graduate School of Logistics, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea
Hye Min Chung: Graduate School of Logistics, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea

Sustainability, 2019, vol. 11, issue 16, 1-18

Abstract: 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.

Keywords: data envelopment analysis (DEA), bi-objective multiple-criteria data envelopment analysis (BiO-MCDEA), cluster analysis; Brazil; port efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2019
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