基于同贝同步装卸的岸桥与集卡协同调度

An Optimization Method for Loading/Unloading Sequence and Truck Dynamic Scheduling Based on Dual-cycling Model in Container Terminal

摘要:

集装箱码头岸边装卸桥同贝同步装卸船模式下，岸桥装卸作业序列决策和集卡调度协同优化是保障同步装卸率的关键，属NP难问题。考虑集卡和岸桥作业之间的紧密衔接、堆场翻箱影响、船舶舱盖等空间约束，构建以岸桥装卸总次数及堆场翻箱次数最小化、岸桥最大完工时间最小化、集卡等待时间最小化为目标的岸桥装卸序列决策与集卡调度联合优化数学模型。设计改进遗传算法?改进非支配排序多目标遗传算法（IGA-INSGA-II）两阶段算法，利用IGA求解船舶贝位内岸桥装卸集装箱作业序列；基于装卸作业序列优化方案和INSGA-II，提出岸桥最早可作业时间优先的集卡任务指派策略，设计分段式编码及解码方法，求解集卡任务分配与指派问题。通过算例，将设计的算法与常见智能算法进行对比分析，验证了模型与算法的有效性。

Abstract:

Under the dual cycling operation of container terminal， the decision-making of quay crane loading and unloading sequence and the collaborative optimization of truck scheduling are the key to dual cycling， which is a NP hard problem. Considering the close relationship between container truck and quay crane operation， the impact of container relocation and the space restraint such as hatch cover， a joint optimization mathematical model of quay crane loading and unloading sequence and truck scheduling was established， which aimed at minimizing the number of empty driving of quay crane and the number of container relocation in yard， minimizing the maximum completion time of the ship and minimizing the waiting time of container trucks. IGA-INSGA-II decision-making algorithm was designed and the improved genetic algorithm （IGA） was used to solve the container loading and unloading sequence at the ship bay； Based on the optimization scheme of loading and unloading sequence， the non-dominated sorting multi-objective genetic algorithm （INSGA-II） was improved， truck dispatching strategy with priority to earliest quay crane operational time was proposed， and the segmented coding and decoding method was designed to solve the truck dispatching problem. The algorithm was compared with intelligent algorithms to verify the effectiveness of the model and algorithm.