Devolution of Sta. Ana Davao Port： Then and Now

Better seaport operation is one of the indicators that a city is achieving its goals towards progress and development. The Sta. Ana Davao Port for example is one of the many seaports in Region XI that caters to thousands of passengers going from one terminal to another and from one island to another. In this study, it will revisit the status of Sta. Davao Port from the time when it was devolved from the national goverrmaent under Philippine Ports Authority （PPA） to local government of Davao City. The devolution was in accordance with the Memorandum of Agreement by two parties, which clearly stipulates the full-control of operation of the local government of Davao City with an end view of bringing more investors, tourist hubs and other business related concerns. This study utilized descriptive-evaluative method using interview guide question as a qualitative approach in gathering data. The participants were the former employees of the PPA and the current management of Sta. Ana Davao Port under the local government. Data gathered were analyzed using Nvivo software for qualitative research and content analysis to obtain factual information direct from the participants. It was found out that most of the employees agreed that the office needs improvement in terms of physical, financial and human aspects. For several years, the passengers of both local and foreign shipping lines were complaining on its limited berthing capacities, saturated container yards and even worse the collapse of the berthing area which resulted to the delay of transactions in the operation of the vessels. In spite of employees claimed that prior to devolution of Sta. Ana Davao Port to the local government of Davao City, the operation was well-managed and maintained, thereby resulted to passengers＇ satisfaction. Thus, the researchers aimed to provide an action plan as basis by the management of the Sta. Davao Port to achieve its vision, missions and goals.

Application of Random Search Methods in the Determination of Learning Rate for Training Container Dwell Time Data Using Artificial Neural Networks

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.

DEA-based Malmquist Productivity Index Measure of Operating Efficiencies:New Insights with an Application to Container Ports

To investigate the long-term operating efficiencies of container ports, we extend the work of previous researches to present a new systemic and improved method of data envelopment analysis （DEA）-based Malmquist productivity index （MPI） in this paper. An approach based on both panel data and multi-inputs/outputs is considered comprehensively, and aims at measuring the operating efficiencies of 10 leading container ports in China from 2001 to 2006 by applying this new systematic influence factor of total factor productivity change is the calculation method. The results illustrate that the main technology change, and the container transportation of these 10 ports is on the healthy development status and will recover and grow reposefully in the following years