LAPSE:2023.10756v1
Published Article

LAPSE:2023.10756v1
Uncertain Network DEA Models with Imprecise Data for Sustainable Efficiency Evaluation of Decentralized Marine Supply Chain
February 27, 2023
Abstract
With the expansion of global trade and the deterioration of the marine environment, research on the sustainability of marine transport has drawn increasing scientific attention. This study takes the marine supply chain composed of Maersk and ports in 17 coastal cities in China as decision-making units (DMUs). It then chooses indicators from the three dimensions of economy, environment and society to evaluate the sustainable efficiency of the marine supply chain, Maersk and ports. In order to deal with the uncertain variables of the sustainability evaluation index, this study develops an uncertain network DEA model based on the uncertainty theory, and the computable equivalent form and proof are also provided. In addition, this study divides the decentralized marine supply chain into two modes, i.e., Maersk as leader and the port as leader, and it calculates their sustainable efficiency, respectively. These results suggest that the sustainable performance of ports is superior to that of Maersk, and the sustainable performance of the marine supply chain is better under the lead of ports, but most of the sustainable efficiencies of marine supply chains are inefficient. Therefore, ports should act as a catalyst for the development of the marine supply chain, and the management implications and suggestions for the economic, environmental, and social dimensions are also outlined at the conclusion.
With the expansion of global trade and the deterioration of the marine environment, research on the sustainability of marine transport has drawn increasing scientific attention. This study takes the marine supply chain composed of Maersk and ports in 17 coastal cities in China as decision-making units (DMUs). It then chooses indicators from the three dimensions of economy, environment and society to evaluate the sustainable efficiency of the marine supply chain, Maersk and ports. In order to deal with the uncertain variables of the sustainability evaluation index, this study develops an uncertain network DEA model based on the uncertainty theory, and the computable equivalent form and proof are also provided. In addition, this study divides the decentralized marine supply chain into two modes, i.e., Maersk as leader and the port as leader, and it calculates their sustainable efficiency, respectively. These results suggest that the sustainable performance of ports is superior to that of Maersk, and the sustainable performance of the marine supply chain is better under the lead of ports, but most of the sustainable efficiencies of marine supply chains are inefficient. Therefore, ports should act as a catalyst for the development of the marine supply chain, and the management implications and suggestions for the economic, environmental, and social dimensions are also outlined at the conclusion.
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Keywords
decentralized marine supply chain, sustainable efficiency, uncertain network DEA model, uncertainty theory
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Suggested Citation
Chi E, Jiang B, Peng L, Zhong Y. Uncertain Network DEA Models with Imprecise Data for Sustainable Efficiency Evaluation of Decentralized Marine Supply Chain. (2023). LAPSE:2023.10756v1
Author Affiliations
Chi E: Department of International Trade and Economy, Ocean University of China, Qingdao 266100, China [ORCID]
Jiang B: Department of International Trade and Economy, Ocean University of China, Qingdao 266100, China
Peng L: Department of International Trade and Economy, Ocean University of China, Qingdao 266100, China
Zhong Y: School of Economics, Ocean University of China, Qingdao 266100, China
Jiang B: Department of International Trade and Economy, Ocean University of China, Qingdao 266100, China
Peng L: Department of International Trade and Economy, Ocean University of China, Qingdao 266100, China
Zhong Y: School of Economics, Ocean University of China, Qingdao 266100, China
Journal Name
Energies
Volume
15
Issue
15
First Page
5313
Year
2022
Publication Date
2022-07-22
ISSN
1996-1073
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PII: en15155313, Publication Type: Journal Article
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LAPSE:2023.10756v1
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https://doi.org/10.3390/en15155313
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Feb 27, 2023
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