Abstract: Aiming at the collaborative berth and quay crane scheduling problem, the idea of “chain optimization” is introduced to analyze the container loading and unloading operation process using the operation chain method. Firstly, the berth plan is taken as the starting chain unit, and the resource node optimization strategy is used for analysis, so as to establish the model with the goal of minimizing the total cost of ships in the port. Then, the quay crane unloading operation is taken as the end chain unit, and the task node optimization strategy is used to analyze, and the model is built with the goal of minimizing the maximum completion time of quay crane. Considering the overall performance of operation chain, a nested loop algorithm is designed to solve the problem. In the inner loop, the genetic algorithm is used to solve the berth quay crane allocation model and the quay crane scheduling model respectively. In the outer loop, the number of quay crane is used as a common variable to transmit and feedback the two models to find the optimal solution of coordinated adjustment. Compared with single scheduling, the results show that the optimization effect of collaborative scheduling is better. Compared with the results of particle swarm optimization, ant colony algorithm and bee colony algorithm, the genetic algorithm is better in solving quality and efficiency, which proves that the proposed model and algorithm can effectively solve this problem.

Key words: container terminal, berth-quay crane collaborative scheduling, operation chain, genetic algorithm

摘要： 针对泊位与岸桥协同调度问题，引入“链式优化”思路，用作业链的方法分析集装箱装卸作业过程，首先将泊位计划作为开始链单元，采用资源节点优化策略进行分析，以最小化船舶在港总成本为目标建立模型；然后将岸桥卸船作业作为结束链单元，采用任务节点优化策略进行分析，以最小化岸桥最大完工时间为目标建立模型。考虑到作业链的整体性能，设计嵌套循环算法进行求解，内循环中用遗传算法分别求解泊位岸桥分配模型和岸桥调度模型，外循环中用岸桥数量作为公用变量对两个模型进行传递和反馈，寻找协同调度最优解。与单独调度进行对比，结果表明协同调度的优化效果更好；与粒子群算法、蚁群算法和蜂群算法的求解结果进行比较，表明遗传算法在求解质量和效率方面都更优，证明了提出的模型和算法能够有效解决此问题。

关键词: 集装箱码头, 泊位岸桥协同调度, 作业链, 遗传算法