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Sustainable competitiveness evaluation of container liners based on granular computing and social network group decision making

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Abstract

In the shipping industry, the sustainability of the shipping companies has received more attention besides the economic benefits. A suitable evaluation model can provide a credible sustainable improvement test and guide the operation and management of shipping companies. This study is concerned with a granular evaluation model for the sustainable competitiveness evaluation of container liners, where the interval linguistic version of the analytic hierarchy process is designed for comparing alternatives comprehensively. To obtain ideal consistent preference relations, we propose a modification method by constructing information granules and using particle swarm optimization. Moreover, based on the combination of individual preference relations and experts’ weights obtained from the social network, the aggregated overall opinion of alternatives is achieved. Furthermore, a case study evaluating the sustainable competitiveness of container liners is conducted to support the proposed evaluation framework’s feasibility and practicality.

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Acknowledgements

The authors are grateful to the editors and the reviewers for their valuable and constructive comments to improve this paper. This work was supported in part by the Natural Science Foundation of China under Grants (Nos. 61773352, 71971051), and in part by the Fundamental Research Funds for the Central Universities (No. 3132019357).

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Authors and Affiliations

  1. School of Science, Dalian Maritime University, Dalian, 116026, People’s Republic of China

    Xueqin Liu, Yanjun Wang & Lidong Wang

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  1. Xueqin Liu
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  2. Yanjun Wang
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  3. Lidong Wang
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Correspondence to Lidong Wang.

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Appendix

Appendix

The relevant information of five container shipping liners is exhibited in Table 3.

Table 3 Container shipping liners competitive attribute information
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Liu, X., Wang, Y. & Wang, L. Sustainable competitiveness evaluation of container liners based on granular computing and social network group decision making. Int. J. Mach. Learn. & Cyber. 13, 751–764 (2022). https://doi.org/10.1007/s13042-021-01325-5

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