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Integrating multi-stage data envelopment analysis and a fuzzy analytical hierarchical process to evaluate the efficiency of major global liner shipping companies

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  • Shih-Liang Chao

Abstract

In this study, we establish a multi-stage data envelopment analysis model to evaluate the efficiency of global liner shipping companies (LSCs). Because conventional solution procedures cannot guarantee the uniqueness of solutions, in this study, we specifically devise a new two-phase algorithm to overcome this problem. The first phase ranks the priority of all stages by applying fuzzy analytical hierarchical process. The second phase then solves the efficiency score for each stage according to its priority. We established and empirically tested a three-stage research model based on data collected from the Containerization International Year Book, the Alphaliner website and annual LSC reports for the year 2012. The results show that the proposed algorithm not only determines unique solutions for the efficiency scores but also determines the priority order of the stages involved in this process. Taking advantage of the proposed model and algorithm, LSCs can effectively locate bottlenecks in their production processes and further improve them by adjusting the values of the corresponding input and output variables. In addition, the priority order of the stages obtained from the empirical study can also help LSCs allocate their resources.

Suggested Citation

  • Shih-Liang Chao, 2017. "Integrating multi-stage data envelopment analysis and a fuzzy analytical hierarchical process to evaluate the efficiency of major global liner shipping companies," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(4), pages 496-511, May.
    • Handle: RePEc:taf:marpmg:v:44:y:2017:i:4:p:496-511
    DOI: 10.1080/03088839.2017.1298863
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    Cited by:

    1. Mohammad Amirkhan & Hosein Didehkhani & Kaveh Khalili-Damghani & Ashkan Hafezalkotob, 2018. "Measuring Performance of a Three-Stage Network Structure Using Data Envelopment Analysis and Nash Bargaining Game: A Supply Chain Application," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(05), pages 1429-1467, September.
    2. H. Pierre Hsieh & Kuo‐Cheng Kuo & Minh‐Hieu Le & Wen‐Min Lu, 2021. "Exploring the cargo and eco‐efficiencies of international container shipping companies: A network‐based ranking approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(1), pages 45-60, January.
    3. Yi-Hui Liao & Hsuan-Shih Lee, 2023. "Using a Directional Distance Function to Measure the Environmental Efficiency of International Liner Shipping Companies and Assess Regulatory Impact," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    4. Chao, Shih-Liang & Yu, Ming-Miin & Hsieh, Wei-Fan, 2018. "Evaluating the efficiency of major container shipping companies: A framework of dynamic network DEA with shared inputs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 44-57.
    5. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    6. Xiuquan Huang & Tao Zhang & Xi Wang & Jiansong Zheng & Guoli Xu & Xiaoshan Wu, 2024. "Regional differences of agricultural total factor carbon efficiency in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.

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