Research On Vehicle Path Planning And Optimization Algorithm In Campus Logistics Distribution

Vehicle path planning is an important part in the logistics distribution system.Especially during the COVID-19 virus prevention and control period,schools,as a mass crowd gathering area,must enforce the close management and administration in order to ensure the health and safety of teachers and students,which will inevitably lead to the lack of staff engaged in the delivery of goods on campus,affecting the delivery efficiency and safety.Thus,it is particularly pivotal to carry out effective and reasonable logistics distribution measures and vehicle route planning.According to the relevant theories of vehicle path planning with time-windows,and the conditions of the logistics center and customer demands,I put forward several relevant assumptions for the purposes of the lowest total distribution cost,which are based on the restrictions of softening time window the load of the distribution vehicles.The mathematical model of VRPTW,Fusion Simulated Annealing and Adaptive Beetle Antennae Search Algorithm were established to justify the rationality of the model and the effectiveness of the algorithm.With the campus of Hei Longjiang University as an example,the algorithm was simulated and optimized,and the algorithm design and experimental verification were completed.(1)Study the problem of vehicle route with time window,analyze the content,classification and related solutions of the vehicle routing problem.(2)Study the basic ideas of Genetic Algorithm,Simulated Annealing algorithm and Beetle Antennae Search Algorithm,as well as their advantages and disadvantages;analyze the influence of the whole operation on the algorithm respectively;finally,discover the optimal parameter combination for solving the problem of vehicle routing with time Windows.(3)Collect and compile the date from Hei Longjiang University,and carry out the simulation experiment by using the Fusion Simulated Annealing and Adaptive Beetle Antennae Search Algorithm;analyze and compare the simulation results.