Integrated Optimization Model For Automated Lifting Vehicles Scheduling And Yard Allocation At Automated Container Terminals

With the acceleration of economic globalization and regional integration,the world trade volume has been growing rapidly.As a major mode of transport for the import and export of seaborne shipping,container shipping has also become more and more important.In order to enhance competitiveness and improve operational efficiency,many container terminals at home and abroad are actively introducing advanced technologies in order to realize the automated operation of terminals.The trend of using fully automated or semi-automated container terminals is becoming more and more obvious.The automated container terminal has entered a new round of rapid development.It is of great significance to study the interrelationship among the operation links of automated container terminals and to integrate and optimize all operation links to improve the overall operation efficiency of the terminals and to enhance the overall competitiveness of container terminals in our country.At present,a lot of scholars have applied modern optimization algorithms to solve the problem of automated container terminal scheduling optimization.However,how to achieve the optimization of each operation link and how to deal with the relationship between the constraints so that the mathematical model is more realistic and the solution algorithm is more efficient,is still the problem of overall optimization of container terminal.Therefore,in view of the current research problems,this paper analyzes the automated container terminal operating system,including terminal layout,operation flow,operation equipment,loading and unloading technology,and analyzes the automated container terminal horizontal handling system and yard operating system;then aiming at the horizontal handling and yard operations of automated container terminals,an integrated scheduling and synchronization optimization method is proposed to minimize the maximum completion time and take into account the constraints of the number of loading and unloading equipment,working time,work order and so on.In order to solve the model,an improved genetic algorithm is designed for the characteristics of the problem studied in this paper.Finally,the algorithm proposed in this paper is compared with the exact solutions and lower bounds in solving the problem of time and solving the quality problems by using different scale examples.The results show that the heuristic proposed in this paper can improve the computational efficiency and find the approximate optimal solution to the large-scale study in a reasonable time.