Evaluation of the European container ports using a new hybrid fuzzy LBWA-CoCoSo'B techniques

Reducing transportation costs is one of today's global supply chains' essential tasks. For this purpose, as the crucial players of the worldwide supply chains, the international container shipping companies try to find optimal ways to reduce the shipping costs. Unnecessary transhipment and displacement operations are the leading causes of high logistics and transport costs. Selecting the most appropriate container seaport is one of the most effective ways to minimize these costs. The proper container port choice can provide an optimum container shipping system consisting of productive, efficient, and less costly container lines. Besides, a methodological framework is needed to address these kinds of highly complex decision-making issues. To serve this purpose, this paper proposes a fuzzy integrated MCDM approach consisting of the Fuzzy LBWA and fuzzy CoCoSo'B techniques. The consistency and stability of the model applied to evaluate the European container ports have been approved by a comprehensive sensitivity analysis. When the obtained results are evaluated, While C5 Port Costs has been determined as the most influential criterion, A2 Port of Antwerp (2.034) is the best and A10 Port of Barcelona (1.266) is the worst alternative. The obtained results were validated by comparing the results of the implemented popular MCDM frameworks with the help of Spearman Correlation Coefficient (SCC) [(SCC (MABAC) = 1.00; SCC (MAIRCA) = 1.00 and SCC (CoCoSo) = 0.915]. All SCC values have over 0.80 that can be accepted high correlation coefficient. The analysis results prove that the proposed fuzzy technique can be implemented to solve the highly complex decision-making problems faced in the maritime industry.

Automated summary

Reducing transportation costs is an important task for global supply chains. This paper proposes a fuzzy integrated MCDM approach to evaluate European container ports and shows that it can be effective in solving complex decision-making problems in the maritime industry.