Research On Double-machine ETV Scheduling Optimization In Airport Freight Area

In recent years,in order to adapt to the high-speed growth of air cargo,many large airports in China have built air cargo terminals.Eighty percent of the goods are containerized goods.These containerized goods are concentrated in and out of Hong Kong at a certain time due to traffic control,flight arrangements,etc,resulting in increasing pressure on cargo transshipment.Therefore,each airport freight station actively introduces highly automated logistics equipment to improve the transshipment efficiency of goods.In the airport freight station,the warehouse three-dimensional is an important node for the transit of containerized goods.Elevating transfer vehicles is an important transshipment logistics equipment for container loading and unloading.Elevating transfer vehicles is an important transshipment logistics equipment for containerized warehouses.However,the efficiency of ETV is low due to the traditional scheduling algorithm.Therefore,studying the scheduling optimization problem of ETV is of great significance to improve the efficiency of airport freight stations.Firstly,the motion process of ETV is analyzed,and uses the 7-segment S-type acceleration and deceleration model to establish the motion model.The concept of task chain is proposed for the task set of ETV scheduling,which simplifies the scheduling model.Then,the scheduling process of single-machine dual-location ETV is studied.A deadlock-free scheduling strategy is proposed for the characteristics of the ETV stage double-board operation,so that the two ETV locations can be fully utilized for transshipment and improve efficiency.In order to solve the problem that the task-based task chain generation algorithm considers complicated and error-prone,a task chain generation algorithm based on ETV stage status is proposed,which simplifies the task chain generation process.The adaptive particle swarm optimization algorithm is used to solve the scheduling problem of single-machine ETV.Compared with the traditional chain scheduling strategy,the transport efficiency of ETV is significantly improved.Finally,on the basis of studying the ETV of single-machine dual-location,the optimized scheduling of ETV with dual-machine dual-station is analyzed.Aiming at the traditional first-come-first-two-machine collision avoidance strategy,a two-machine dynamic collision avoidance and time equalization strategy is proposed.This strategy ensures that the two machines can balance the time required to complete their respective tasks without interfering with each other and colliding in order to improve the transfer efficiency of the two machines.Quantum particle swarm optimization is used to solve the ETV scheduling problem,and a linear factor is introduced to enhance the global search ability of the particle.According to the improved precocious judgment mechanism,when the particles appear precocious,using the chaos operator perturbs the global optimal position of the particle to update the position of the particle to make it jump out of the current local optimum to search for other areas of the solution space.According to the characteristics of the integer solution of the ETV scheduling problem,the tabu search strategy is introduced on the basis of the optimized quantum particle swarm optimization algorithm,which improves the particle optimization ability.Compared with various optimization algorithms,the improved quantum particle swarm optimization algorithm has fast convergence speed and strong global optimization ability.Compared with the traditional chain scheduling strategy,the improved algorithm has improved the transshipment efficiency,and better solves the problem of low efficiency of dual-machine ETV.