Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 1

TL;DR

This paper introduces a novel data-driven scheduling approach combining enhanced particle swarm optimization and firefly algorithms to significantly improve port efficiency and reduce vessel wait and turnaround times.

Contribution

It proposes a new coordinative scheduling method using advanced metaheuristics and compares batch and rolling horizon paradigms for port terminal optimization.

Findings

Average wait time reduced by 86.0% - 95.5%.

Average turnaround time saved by 38.2% - 42.4%.

Rolling horizon method reduces runtime to 20 mins from 4 hrs.

Abstract

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.