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With the rapid development of global shipping, port cargo volumes are increasing significantly, leading to growing issues of vessel congestion and delays, which in turn severely constrain port operations. In response to the challenges posed by surging cargo volumes, ship congestion, and aggravated pollution, this paper proposes a Time-Berth & Pollution (TB&P) model. The model takes the difference between the actual and expected time in port as the objective function, aiming to minimize operating costs and pollution emissions, subject to constraints related to time, space, and machinery/equipment. To solve the TB&P model, an improved version of the basic Beluga Whale Optimization (BWO) algorithm is developed, termed the Opposition Learning Beluga Whale Optimization (OBWO) algorithm. The feasibility and superiority of the proposed model and improved algorithm are verified through case data from a port. Results demonstrate that, compared with traditional models, the established model significantly reduces the extent of ship delays and mitigates water pollution in the port area. Furthermore, the proposed OBWO algorithm exhibits enhanced stability and accuracy relative to other selected algorithms.

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随着全球航运的快速发展，港口货运量与日俱增，船舶拥堵、延误现象逐渐严重，港口的运行也受到了严重的制约，因此本文针对当前港口货运量激增、船舶拥堵、污染加重的问题，提出了一种以船舶实际在港时间与期望在港时间差值最小、运营成本和污染排放最低为目标函数，以时间、空间、机械设备等限制为约束条件的港口调度规划（TB&P）模型；为了求解TB&P模型，对基础白鲸算法（BWO）提出改进，设计一种反向学习白鲸算法（OBWO）对TB&P模型进行求解；通过港口实例数据验证优化模型和改进算法的可行性与优越性。验证结果表明，建立的模型相较于传统模型，能够降低船舶的延误程度30%以上，同时减少港口内水域的污染；提出的求解算法与本文选取的算法相比，求解精度提高40%以上。

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