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Berth allocation problem with uncertain vessel handling times considering weather conditions

机译:考虑到天气条件的不确定船舶处理时间泊位分配问题

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摘要

The vessel handling times at container terminals can be inevitably affected by weather conditions. This study proposes a berth allocation problem (BAP) with vessel handling time uncertainty considering the impact of weather conditions, which is seldom considered in the previous studies on BAPs. A two-stage optimization method, which focuses on the evaluation of vessel handling time under different weather conditions, is developed for addressing our BAP. In stage I, we determine the vessel handling times considering the influence of weather conditions. Based on the vessel handling times obtained in stage I, stage II presents a mixed-integer programming (MIP) model for solving the BAP. Moreover, an efficient particle swarm optimization algorithm embedded with machine learning approach is devised for solving the BAP model in large-scale problem cases. Numerical experiments are carried out to assess the effectiveness of the proposed model and the efficiency of the proposed algorithm.
机译:容器终端的船舶处理时间可以不可避免地受天气条件影响。 本研究提出了考虑到天气条件的影响的船舶处理时间不确定性,这提出了船舶处理时间不确定性,这很少考虑在以前的排骨上的研究中。 一种两级优化方法,专注于在不同天气条件下进行船舶处理时间的评估,以寻址我们的BAP。 在阶段,我们确定考虑到天气条件的影响的船舶处理时间。 基于在阶段I中获得的血管处理时间,阶段II呈现用于求解BAP的混合整数编程(MIP)模型。 此外,设计了一种嵌入机器学习方法的有效粒子群优化算法,用于解决大规模问题情况下的BAP模型。 进行了数值实验,以评估所提出的模型的有效性和所提出的算法的效率。

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