自动化码头双小车岸桥与AGV协调调度问题研究  

Research on Problem of Double-Trolley Quay Crane and AGV Coordinated Scheduling in Automated Terminal

作　　者：梁承姬 林洋 LIANG Chengji;LIN Yang(Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China)

机构地区：[1]上海海事大学物流科学与工程研究院,上海201306

出　　处：《计算机工程与应用》2019年第10期256-263,共8页Computer Engineering and Applications

基　　金：国家自然科学基金(No.71471110);上海市科委创新项目(No.16040501500;No.16DZ1201402);上海市重点学科资助项目(No.J50604);陕西省社会科学基金(No.2015D060)

摘　　要：为研究自动化集装箱码头中自动导引运输车(Automated Guided Vehicle,AGV)与双小车岸桥(DoubleTrolley Quay Crane,QC)的协调调度问题,考虑双小车岸桥中转平台及其容量限制,并以双小车岸桥门架小车时间窗为约束,建立以集装箱任务最大完工时间最小化为目标的混合整数规划模型。设计启发式算法,由中转平台的容量求得岸桥门架小车操作集装箱任务的时间窗,并采用遗传算法进行求解,给出相应的AGV调度优化方案,解决两大设备的协调调度问题。最后,以10组实验为例,比较了遗传算法与粒子群算法的优化结果。结果表明两种算法一致,且基于遗传算法的模型求解收敛速度更快,从而验证了该算法的可行性。In order to study the coordinated scheduling of Automated Guided Vehicle(AGV)and Double-Trolley Quay Crane(QC)in automated container terminals, considering the double-trolley pontoon bridge transfer platform and its capacity constraints, and with the constraints of the Portal Trolley(PT)time window, a mixed-integer programming model is established with the goal of minimizing the maximum completion time of container tasks. A heuristic algorithm is designed to obtain the time window of the quayside gantry crane operation container task from the capacity of the transfer platform. The genetic algorithm is used to solve the AGV scheduling optimization program and the coordination scheduling problem of the two major equipments. Finally, 10 groups of experiments are used as examples to compare the optimization results of genetic algorithm and particle swarm optimization. The results show that the two algorithms are consistent, and the convergence speed of the model based on genetic algorithm is faster, thus verifying the feasibility of this method.

关 键 词：自动化集装箱码头 双小车岸桥 中转平台 时间窗 AGV调度 遗传算法 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]