The impact of AI technology adoption on operational decision-making in competitive heterogeneous ports☆

Xu, Haonan; Liu, Jiaguo; Xu, Xiaofeng; Chen, Jihong; Yue, Xiaohang

doi:10.1016/j.tre.2024.103428

Cited by 11 publications

(1 citation statement)

References 19 publications

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“…AI, IoT and blockchain technology can be used for many applications [48,49]. According to the findings of a study by Xu et al [50], the adoption of AI technology by various ports has the potential to increase port profitability; however, the unfortunate reality is that the simultaneous adoption of this technology makes homogenised competition even more intense, which poses a risk to the realisation of profits. In addition, though a hub port has the ability to harness the benefits of AI to increase its competitiveness, it also has the potential to harm the performance of rivals and society as a whole.…”

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confidence: 99%

Leveraging Artificial Intelligence to Enhance Port Operation Efficiency

Dinh,

Pham,

Nguyen

et al. 2024

Polish Maritime Research

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Maritime transport forms the backbone of international logistics, as it allows for the transfer of bulk and long-haul products. The sophisticated planning required for this form of transportation frequently involves challenges such as unpredictable weather, diverse types of cargo kinds, and changes in port conditions, all of which can raise operational expenses. As a result, the accurate projection of a ship’s total time spent in port, and the anticipation of potential delays, have become critical for effective port activity planning and management. In this work, we aim to develop a port management system based on enhanced prediction and classification algorithms that are capable of precisely forecasting the lengths of ship stays and delays. On both the training and testing datasets, the XGBoost model was found to consistently outperform the alternative approaches in terms of RMSE, MAE, and R2 values for both the turnaround time and waiting period models. When used in the turnaround time model, the XGBoost model had the lowest RMSE of 1.29 during training and 0.5019 during testing, and also achieved the lowest MAE of 0.802 for training and 0.391 for testing. It also had the highest R2 values of 0.9788 during training and 0.9933 during testing. Similarly, in the waiting period model, the XGBoost model outperformed the random forest and decision tree models, with the lowest RMSE, MAE, and greatest R2 values in both the training and testing phases.

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confidence: 99%