Research On Cross Docking Vehicle Scheduling And Routing Optimization Based On Improved Genetic Algorithm

With the maturity of Internet technology,the e-commerce industry is developing faster and faster,and the development of e-commerce is inseparable from the development of logistics.Logistics distribution technology has played a huge role in the development of logistics companies.Logistics companies have taken advantage of cross-database technology to reduce logistics and distribution costs in order to gain advantages in competition.The cross-docking technology is a process in which trucks arrive at the center of the warehouse,and the trucks are dispatched to the warehouse door order,and the goods are unloaded,sorted and selected.In the study of the cross-docking,the truck receives the goods into the warehouse and the truck outbound distribution process is an indispensable part.Therefore,studying the truck transportation route has also become the research content of the library.Nowadays,there are more and more types of customers' needs.In order to meet the high requirements of customers,the distribution process of trucks is becoming more and more complicated and diversified.Choosing the number of delivery trucks and what kind of delivery route is an important means to control the distribution cost during the cross-docking process.Aiming at the problem of warehouse truck routing and route optimization,this paper constructs a mathematical model with the minimum operating cost as the objective function,and uses the warehouse door allocation constraint,vehicle route constraint and truck load constraint as the main constraints,and designs the improved genetic algorithm to solve the problem.The obtained truck door assignment and vehicle transportation route results are drawn into a table for analysis and comparison.In order to avoid the excessive convergence of the genetic algorithm,a new crossover method is constructed in the algorithm design,which can better retain the genes on the chromosome and pass the retained genetic genes to the next generation.In the solution process,the actual geographical location and quantity of suppliers and customers are known and determined,the type of goods is 4,and the cargo volume of goods is 110.Three different problem scales were set up,and two solution schemes were proposed to obtain the order of the trucks to distribute t he warehouse doors and the truck transportation route.By comparison,it is found that although the results of the two schemes are the same for the scale of the problem 1,for the problem scales 2 and 3,the vehicle transportation cost of the scheme 1 is reduced by 32% and 24% respectively compared with the scheme 2,and the transportation time is reduced respectively.7%,12%.In this paper,the problem of cross-docking scheduling and path optimization based on improved genetic algorithm is studied.The mathematical model and optimization algorithm can be developed and designed into a vehicle management system to support the actual operation of the library.