Optimization Method For AGV Real-time Scheduling In Automated Container Terminals

With the rapid growth of international container trade,container terminals play an important role in the global shipping industry.To solve the problems of large-scale ships and rising labor costs,the port has gradually turned to the construction of automated or semi-automated container terminals.The automated guided vehicle(AGV)is the main transportation equipment between the quayside and the yard-side in the automated container terminal.Its operational efficiency is one of the key factors affecting the quality of the automated terminal service.Designing an efficient AGV dispatch plan,improving the efficiency of terminal operations and shortening the time of ships in terminal are urgent problems to be solved in automated container terminals.This paper focuses on the task assignment and path planning selection of AGV real-time scheduling in automated container terminals.Firstly,for the task assignment problem of AGV scheduling,the order of the quay crane operation is unknown.To minimize the weighted sum of the AGV unload travel distance and the average quay crane work time,a real-time scheduling algorithm is proposed whose strategy can update according to the status change.A scheduling strategy based on preference function is designed.The optimal container is compared with other containers to form training set,and the deep learning model is used to complete the real-time updating of the scheduling strategy.Secondly,for the task assignment and path planming selection problem of AGV scheduling,a mixed integer programming model is constructed with the goal of minimizing the makespan,where the order of quay crane operation is known.Considering the congestion caused by AGV conflict,a real-time scheduling algorithm is designed to complete the allocation of AGV scheduling operation scheme.Taking an automated container terminal as an example,the results verify the effectiveness and superiority of the scheme,which provides a new idea for improving the efficiency of equipment operation and the service level in automated container terminal.