Optimization Of L-AGV Task Scheduling Under The Dual-Cycling Operation At Automated Terminals

Automated container terminal(ACT)is a system to be optimized with complex scheduling problems.The scheduling of loading and unloading system is a key link affecting the overall operating efficiency of the terminal.L-AGV is a horizontal transport equipment with high degree of automation,good connection ability with quay cranes(QC)and yard cranes(YC),and low energy consumption in ACT.It is equipped with container supports under QC and at the front of the yard block to complete the horizontal transport of containers.The operation mode of QC and the scheduling mode of L-AGV have an impact on the reasonable dispatch of L-AGV to respond the transport task demands of QC and YC.This paper studies the L-AGV task scheduling optimization problem under the double-cycle operation,which is of great practical significance in helping enterprises to make efficient use of equipment resources and improve terminal operation efficiency.In this paper,the loading and unloading system of ACT is clarified,the operation flow of import and export is described,the advantages and disadvantages of the QC’s operation modes and vehicle’s dispatching modes are analyzed.L-AGV is selected as the scheduling optimization object,the task schedule problem is under the dual-cycling operation and the pool strategy.To minimize ship’s berth time,this paper formulate the problem as a mixedinteger programming(MIP)model based on the flexible flow-shop scheduling theory.The optimization objective is to minimize QC’s makespan,the main constraint is the time constraint when the equipment continuously performs the task.The simulated annealing algorithm can avoid the premature convergence of the genetic algorithm into the local optimal solution.An improved genetic algorithm with simulated annealing is designed in the form of matrix coding and solved by MATLAB mathematical analysis software.The improved genetic algorithm is proved to be more effective in global optimization by comparing the performance of the algorithm before and after simulated annealing.Numerical experiments show that the model and the improved genetic algorithm have good adaptability and efficiency.This paper provides a model reference for the reasonable equipment resource allocation and loading and unloading scheduling plan for ACT.