针对中断事件引发延误的集装箱班轮船期恢复问题（VSRP），本文引入航运碳排放交易机制，以最小化航次船期恢复成本、最大化客户服务水平为双目标构建 VSRP 优化模型。针对预期运营中断场景，求解航速调整、跳港及换港操作的最优组合策略，并设计改进自适应非支配遗传排序算法（NSGA-II）对模型进行求解。多组情景算例验证了本文所提模型与改进算法的有效性。数值算例结果表明，不同预期中断时长对应差异化的船期恢复优化组合策略；在无偏好双目标优化下，利用预期中断信息可平均削减 18.31% 的航次恢复成本。敏感性分析表明：碳交易价格上涨时，若碳减排目标偏好权重较大，船期恢复成本与船舶碳排放量均同步下降，碳配额增加将直接降低船期恢复成本；而当碳减排目标偏好权重较小时，碳排放量呈先降后升的变化规律。相关研究结论可为航运企业制定运营中断下的船期恢复方案提供理论支撑与决策依据。

This paper addressed the vessel schedule recovery problem (VSRP) for container liners suffering delays caused by disruptive incidents, and introduced the shipping carbon trading mechanism. A dual-objective optimization model for VSRP was established to minimize voyage schedule recovery costs and maximize customer service level. For scenarios with anticipated operational disruptions, the optimal combined strategy consisting of speed adjustment, port skipping and port swapping was solved. An improved adaptive non-dominated sorting genetic algorithm II (NSGA-II) was designed to solve the proposed model. Multiple scenario-based numerical examples verify the effectiveness of the proposed model and improved algorithm in this paper. Numerical results show that different durations of anticipated disruptions correspond to distinct optimal combined strategies for schedule recovery. Under the dual-objective optimization without preference, utilizing information on anticipated disruptions can reduce voyage recovery costs by 18.31% on average. Sensitivity analysis shows that when the carbon trading price rises, both the schedule recovery cost and vessel carbon emissions decrease simultaneously if the preference weight of the carbon emission reduction objective is large. Besides, an increase in carbon quotas will directly cut down the schedule recovery cost. In contrast, when the preference weight of the carbon reduction objective is small, carbon emissions follow a trend of decreasing first and then increasing. The research conclusions can provide theoretical support and decision-making references for shipping enterprises to formulate schedule recovery schemes under operational disruptions.