College of Navigation, Jimei University, Xiamen, Fujian, China.
PLoS One. 2024 Feb 26;19(2):e0296966. doi: 10.1371/journal.pone.0296966. eCollection 2024.
With the continuous prosperity of maritime transportation on a global scale and the resulting escalation in port trade volume, tugboats assume a pivotal role as essential auxiliary tools influencing the ingress and egress of vessels into and out of ports. As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. However, a majority of current tugboat scheduling models tend to focus solely on the maximum operational time. Alternatively, the formulated objective functions often deviate from real-world scenarios. Furthermore, prevailing scheduling methods exhibit shortcomings, including inadequate solution accuracy and incompatibility with integer programming. Consequently, this paper introduces a novel multi-objective tugboat scheduling model to align more effectively with practical considerations. We propose a novel optimization algorithm, the Improved Grey Wolf Optimization (IGWO), for solving the tugboat scheduling model. The algorithm enhances convergence performance by optimizing convergence parameters and individual updates, making it particularly suited for solving integer programming problems. The experimental session designs several scale instances according to the reality of the port, carries out simulation experiments comparing several groups of intelligent algorithms, verifies the effectiveness of IGWO, and verifies it in the comprehensive port area of Huanghua Port to get the optimal scheduling scheme of this port area, and finally gives management suggestions to reduce the cost of tugboat operation through sensitivity analysis.
随着全球范围内海上运输的持续繁荣以及港口贸易量的不断增加，拖船作为影响船舶进出港的重要辅助工具，发挥着关键作用。因此，优化港口拖轮调度至关重要，因为它有助于提高船舶运输效率、降低港口运营成本，并促进海上运输领域的可持续发展。然而，目前大多数拖轮调度模型往往只关注最大作业时间。或者，制定的目标函数往往与实际情况不符。此外，现有的调度方法存在一些缺点，包括解决方案的准确性不足和不兼容整数规划。因此，本文引入了一种新的多目标拖轮调度模型，以更有效地考虑实际情况。我们提出了一种新的优化算法，即改进的灰狼优化算法（IGWO），用于求解拖轮调度模型。该算法通过优化收敛参数和个体更新来提高收敛性能，特别适合求解整数规划问题。实验部分根据港口实际情况设计了几个规模实例，进行了几组智能算法的仿真实验，验证了 IGWO 的有效性，并在黄骅港综合港区进行了验证，得到了该港区的最优调度方案，最后通过敏感性分析给出了降低拖轮作业成本的管理建议。
