Optimization Of Container Terminals With Double Cycling

In order to improve the terminal efficiency and reduce the terminal cost,quay crane double cycling has been applied in the ports at home and abroad.It enables simultaneous loading and unloading operations by changing the quayside operations without the need for additional equipment.At present,many scholars have studied on the job scheduling of quay cranes with double cycling.However,double cycling not only changed the working sequence of the quay cranes,it changed the routes of the trucks as well.The overall terminal operational efficiency depends on each sub-operation and the effective connections among them.How to reduce the congestion in container terminals and achieve coordinated operations has always been a concern for scholars.Based on the existing research,this dessertation proposed a queuing service network of double cycling operations.The purpose is to analyze the overall efficiency of double cycling and each sub-operation.There are two main aspects:first,modeling the double cycling with queuing networks and analyzing the overall efficiency of double cycling and its impacts on each sub-operations.The key factors that affect the terminal efficiency are analyzed as well.Second,in order to increase handling efficiency,resource allocation,terminal layout and handling processes are optimized from the microscopic to the macroscopic level.First,double cycling is modeled by a queuing network.A closed queuing network is used to model the operations of double cycling.Truck waiting time and terminal productivity are chosen as indicators to illustrate the efficiency of double cycling.In order to analyze the effect of the variance of work time on the overall terminal efficiency,a queuing model with general distribution time is used to model the double cycling.Numerical experiments are given to verify the effectiveness of the proposed model and algorithm and to provide suggestions for improving the terminal efficiency.Second,in order to schedule the amount of equipment,this dessertation establishes the resource allocation model of double cycling.The quantity of equipment is optimized with the goal of minimizing the terminal cost.The objective function is written as convex function of the number of equipment,and it is theoretically proved that there must be one or two solutions to minimize the terminal cost.Finally,by analyzing the operations of double cycling,the bottleneck operations that bounds the overall operation efficiency of the terminal is revealed,which has laid the theoretical foundation for the terminal to improve the efficiency of double cycling.Next,in order to improve the truck efficiency of double cycling and decrease the truck travelling distance,this dessertation proposed a yard layout design model with double cycling.Mixed storage strategies are designed,which storage import and export containers in the same yard block.The yard crane is applied with double cycling techniques to incorporate with quay crane double cycling.The impacts of mixed storage strategies are analyzed from three aspects,truck travel routes,the number of trucks and the operation time of yard cranes.The storage stacking principles are provided to maximum the number of double cycling of yard cranes.And simulation is used to illustrate the efficiency of stacking principles.Finally,this dessertation takes two typical handling systems as examples to analyze the efficiency from the point of handlinig process.The ALV operation process was characterized by a hybrid queuing network,and the road traffic congestion function was used to describe the regularity of the horizontal transport time affected by the number of road vehicles.At the same time,a cost analysis model was established to analyze the net present value of AGV and ALV in one life cycle.By comparing the operating efficiency and the operating cost,indicators of the AGV operating system and the ALV operating system under different quantities are obtained,which provides a basis for selecting the operating equipment for the terminal and optimizing the operation process.