Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 1
This addresses efficiency improvements for port operations, but it appears incremental as it builds on existing optimization methods like particle swarm and firefly algorithms.
The study tackled the problem of optimizing coordinative schedules for tanker terminals to enhance port efficiency, resulting in significant reductions: average wait time decreased by 86.0% - 95.5% and average turnaround time saved 38.2% - 42.4% compared to historical benchmarks.
In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing average wait time and turnaround time. The proposed approach consists of enhanced particle swarm optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global optimal search. Two paradigm methods of the proposed approach are investigated, which are batch method and rolling horizon method. The experimental results show that both paradigm methods of proposed approach can effectively enhance port efficiency. The average wait time could be significantly reduced by 86.0% - 95.5%, and the average turnaround time could eventually save 38.2% - 42.4% with respect to historical benchmarks. Moreover, the paradigm method of rolling horizon could reduce to 20 mins on running time over 3-month datasets, rather than 4 hrs on batch method at corresponding maximum performance.