自动化集装箱码头多层设备调度及仿真分析

doi:10.3778/j.issn.1002-8331.2207-0208

摘要/Abstract

摘要： 为提高集装箱在码头内部的周转效率，研究自动化集装箱码头多层设备作业协调问题。详细分析了集装箱从堆场出发，依次经过场桥、AGV、岸桥门架小车、中转平台、岸桥主小车后到达船上这一过程中多层设备联合作业时的各种状况及运作规则，构建了装船任务的时空流动图。考虑了上述流程中设备的互相等待时间、箱子进出中转平台次序、中转平台容量等约束，建立了以最大完工时间最小化为目标的多层设备集成调度模型。分别使用自适应遗传算法、禁忌搜索算法、模拟退火算法求解出场桥、AGV、双小车岸桥的多层设备联合调度方案。将各算法求解的调度方案在plant-simulation仿真场景中进行实施并对比，自适应遗传算法所求结果优于其他两种算法的概率为81%，自适应遗传算法运行时间优于禁忌搜索算法和模拟退火算法的概率分别为100%和69%。在此基础上，在仿真场景中提出了随机路线、固定路线、分组随机路线三种AGV运作策略，通过比较三种策略下完工时间、设备等待时间、拥堵次数发现算法在分组随机路线的策略下效果最佳。

关键词: 自动化码头, 多层设备调度, 运作规则, 仿真分析, AGV运作策略

Abstract: In order to improve the efficiency of container turnovers within the automated terminals, a coordination problem of multi-layer equipment operation is considered. Starting from the containers departing from the yard, the conditions and operational rules of containers transferring among yard crane, AGV, quay crane gantry trolley, transfer platform and quay crane main trolley to the ship are analyzed in detail for the multi-layer equipment joint operation problem. A time-space flow diagram of the above process is proposed. Considering the mutual waiting time of equipment, the order of containers entering and leaving the transfer platform, and the capacity of the transfer platform as the constraints, a multi-layer equipment integration scheduling model is formulated with the goal of minimizing the maximum completion time. The multi-layer equipment joint scheduling problem is solved by adaptive genetic algorithm（AGA）, Tabu search algorithm （TS） and simulated annealing algorithm（SA）, respectively. By implementing the algorithms in a plant-simulation scene and results comparison, the probability of the AGA algorithm outperforming the other two algorithms in all experiments is 81%. The probability that the running time of the AGA algorithm is better than that of the TS algorithm and that of the SA algorithm is 100% and 69%, respectively. Based on the above research, three AGV operation strategies are designed in the simulation scene, namely, random route, fixed route and group random route. The completion time, equipment waiting time and congestion time of the three strategies are compared, and the algorithm achieves the best results under the strategy of group random route.

Key words: automated container terminal, multi-layer equipment scheduling, operating rules, emulation analysis, AGV operation strategy

王盼龙, 梁承姬, 王钰. 自动化集装箱码头多层设备调度及仿真分析[J]. 计算机工程与应用, 2023, 59(24): 345-359.

WANG Panlong, LIANG Chengji, WANG Yu. Multi-Layer Equipment Scheduling and Simulation Analysis of Automated Container Terminal[J]. Computer Engineering and Applications, 2023, 59(24): 345-359.

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