Purpose: This study aimed to enhance the prediction of container dwell time, a crucial factor for optimizing port operations, resource allocation, and supply chain efficiency. Determining an optimal learning rate for ...Purpose: This study aimed to enhance the prediction of container dwell time, a crucial factor for optimizing port operations, resource allocation, and supply chain efficiency. Determining an optimal learning rate for training Artificial Neural Networks (ANNs) has remained a challenging task due to the diverse sizes, complexity, and types of data involved. Design/Method/Approach: This research used a RandomizedSearchCV algorithm, a random search approach, to bridge this knowledge gap. The algorithm was applied to container dwell time data from the TOS system of the Port of Tema, which included 307,594 container records from 2014 to 2022. Findings: The RandomizedSearchCV method outperformed standard training methods both in terms of reducing training time and improving prediction accuracy, highlighting the significant role of the constant learning rate as a hyperparameter. Research Limitations and Implications: Although the study provides promising outcomes, the results are limited to the data extracted from the Port of Tema and may differ in other contexts. Further research is needed to generalize these findings across various port systems. Originality/Value: This research underscores the potential of RandomizedSearchCV as a valuable tool for optimizing ANN training in container dwell time prediction. It also accentuates the significance of automated learning rate selection, offering novel insights into the optimization of container dwell time prediction, with implications for improving port efficiency and supply chain operations.展开更多
提出了一种考虑集装箱停留时间的堆垛决策支持系统(decision support system considering dwell time,DSS-DT),利用集装箱停留时间的历史数据信息预测拟出场集装箱的停留时间。基于此,针对水平堆垛制定了区域内集装箱堆垛策略,利用数学...提出了一种考虑集装箱停留时间的堆垛决策支持系统(decision support system considering dwell time,DSS-DT),利用集装箱停留时间的历史数据信息预测拟出场集装箱的停留时间。基于此,针对水平堆垛制定了区域内集装箱堆垛策略,利用数学模型和启发式算法选择集装箱场箱位,并通过仿真模拟验证了系统的有效性。结果表明:DSS-DT可明显减少再搬运次数,降低运营成本,提升港口服务水平。展开更多
文摘Purpose: This study aimed to enhance the prediction of container dwell time, a crucial factor for optimizing port operations, resource allocation, and supply chain efficiency. Determining an optimal learning rate for training Artificial Neural Networks (ANNs) has remained a challenging task due to the diverse sizes, complexity, and types of data involved. Design/Method/Approach: This research used a RandomizedSearchCV algorithm, a random search approach, to bridge this knowledge gap. The algorithm was applied to container dwell time data from the TOS system of the Port of Tema, which included 307,594 container records from 2014 to 2022. Findings: The RandomizedSearchCV method outperformed standard training methods both in terms of reducing training time and improving prediction accuracy, highlighting the significant role of the constant learning rate as a hyperparameter. Research Limitations and Implications: Although the study provides promising outcomes, the results are limited to the data extracted from the Port of Tema and may differ in other contexts. Further research is needed to generalize these findings across various port systems. Originality/Value: This research underscores the potential of RandomizedSearchCV as a valuable tool for optimizing ANN training in container dwell time prediction. It also accentuates the significance of automated learning rate selection, offering novel insights into the optimization of container dwell time prediction, with implications for improving port efficiency and supply chain operations.
文摘提出了一种考虑集装箱停留时间的堆垛决策支持系统(decision support system considering dwell time,DSS-DT),利用集装箱停留时间的历史数据信息预测拟出场集装箱的停留时间。基于此,针对水平堆垛制定了区域内集装箱堆垛策略,利用数学模型和启发式算法选择集装箱场箱位,并通过仿真模拟验证了系统的有效性。结果表明:DSS-DT可明显减少再搬运次数,降低运营成本,提升港口服务水平。
