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

• URL: http://arxiv.org/abs/2204.03899v1
• Date: Fri, 8 Apr 2022 07:56:32 GMT
• Title: Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 1
• Authors: Deqing Zhai and Xiuju Fu and Xiao Feng Yin and Haiyan Xu and Wanbing Zhang and Ning Li
• Abstract summary: The proposed approach consists of enhanced particle swarm optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global optimal search. The experimental results show that both paradigm methods of proposed approach can effectively enhance port efficiency.
• Score: 4.181498820782148
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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