Abstract: To make efficient use of terminal resources and improve the operation efficiency of container terminals, based on the background of large-scale transshipment ports, aiming at the tactical decision-making problem of terminal resource allocation, considering the interaction of ship schedule stability, inter-ship container transshipment flow, berth scheduling and yard allocation, and based on the terminal active management strategy, a terminal multi-resource collaborative optimization model is established with the objective of minimizing the completion time of all container transshipment tasks. Based on this, an artificial immune algorithm based on tabu search is designed to solve the model. The effectiveness of the model and algorithm is verified by six numerical experiments of different scales and the comparison experiments with genetic algorithm and artificial immune algorithm. The comprehensiveness and superiority of the collaborative scheduling model are tested by case comparison experiments in different scenarios. The results show that the minimum transit time can be shortened by 4.33% and the maximum can be shortened by 20.19%. It provides an effective decision-making idea for the design of multi-resource allocation template of container terminal.

Key words: container terminal, transshipment operation, integrated scheduling, ship schedule, berth, yard

摘要： 为高效利用码头资源，提高集装箱码头作业效率，以大型转运港为背景，针对码头资源分配的战术决策问题，考虑到船期表稳定性、船舶间集装箱转运流量、泊位调度和堆场分配的相互影响，基于码头主动管理策略，建立了以所有集装箱转运任务完成时间最小化为目标的码头多资源协同优化模型。在此基础上，设计了一种基于禁忌搜索的人工免疫算法对模型进行求解，通过六种不同规模的数值实验，以及与遗传算法和人工免疫算法的比较实验，验证了模型和算法的有效性。通过多情景案例分析，检验了协同调度模型的全面性和优越性，情景分析结果表明，综合决策模型最小可缩短4.33%的转运作业时间，最大可缩短20.19%，为集装箱码头多资源分配提供了有效的解决方法。

关键词: 集装箱码头, 转运作业, 集成调度, 船期表, 泊位, 堆场