自动化集装箱码头AGV配置与调度耦合问题研究

针对自动导引小车(Automated Guided Vehicle,AGV)数量偏多导致的自动化码头水平运输区域拥堵的情况,采用多学科变量耦合优化设计的方法对自动化码头AGV调度与AGV配置问题进行研究。先以最小化岸边等待时间为目标建立AGV调度模型,再以最小化AGV数量为目标建立AGV配置模型。并将完工时刻和AGV数量作为公用设计变量连接两个模型,建立了协调调度耦合模型。设计算例,利用遗传算法(Genetic Algorithm,GA)收敛速度快的特点对该耦合模型进行求解,经反复迭代计算后得出最优AGV数量与AGV调度方案。最后,扩大算例规模,设计9组实验,比较了GA、粒子群算法(Particle Swarm Optimization,PSO)和蚁群算法(Ant Colony Optimization,ACO)的求解结果,结果表明随着算例规模的增大,GA的求解能力更为突出,从而验证了设计的算法的可行性。

Research on Coupling Problem of AGV Configuration and Scheduling in Automated Container Terminal

Aiming at the congestion of the horizontal transportation area of the automated terminal caused by the large number of Automated Guided Vehicles（AGVs）, the multi-disciplinary variable coupling optimization design method is used to study the AGV scheduling and AGV configuration of the automated terminal. Firstly, the AGV scheduling model is established with the goal of minimizing the shore waiting time, and then the AGV configuration model is established with the goal of minimizing the number of AGVs. The two models are connected with the completion time and the number of AGV as common design variables, and a coordinated scheduling coupling model is established. The design example is used to solve the coupled model by using the fast convergence function of Genetic Algorithm（GA）. After repeated iterative calculation, the optimal AGV number and AGV scheduling scheme are obtained. Finally, the scale of the study is expanded, and nine sets of experiments are designed. The results of GA, Particle Swarm Optimization（PSO） and Ant Colony Optimization（ACO） are compared. The results show that with the increase of the scale of the example, the ability to solve GA is more prominent, which verifies the feasibility of the algorithm designed in this paper.