Research On Distribution Vehicle Scheduling Problem And Distribution Center Location Problem Of Agricultural Product Logistics Based On Hybrid Particle Swarm Optimization

The continuous improvement of China's economic level has made the material demand of the people growing,which puts higher demands on agricultural products logistics.China's agricultural product logistics still faces problems in circulation,optimization of distribution vehicle scheduling,and location selection and management of distribution centers.Study the problem of vehicle dispatching optimization of agricultural product logistics and distribution and the location problem of agricultural product distribution center,comprehensively considering various factors,scientifically and rationally establishing a modern and informationized agricultural product logistics distribution center,and then formulating a reasonable and efficient scheduling plan,not only can be punctual,fast,high quality the completion of agricultural product distribution tasks,reducing the cost of agricultural product distribution,but also increase the profitability of agricultural product distribution centers,and promote the development of regional economy.In addition,the efficient circulation rate of agricultural products enables the agricultural products to be preserved,thus ensuring the value of the agricultural products themselves,and for the consumers of agricultural products,they can meet their needs very well.For agricultural producers,they can also get more benefits.The particle swarm optimization algorithm is a simple algorithm with wide application and wide application.This paper improves the particle swarm optimization algorithm and applies it to the problem of distribution vehicle scheduling optimization of agricultural product logistics and the location of agricultural product distribution center.It has certain theoretical significance and practical application value.This paper first introduces the domestic and international research status of agricultural product logistics distribution vehicle scheduling optimization problem and agricultural product logistics distribution center location problem.For the problem of vehicle scheduling optimization of agricultural product logistics distribution,the components and classification of the problem are analyzed.Some algorithms for solving this problem are introduced and the optimization model of this problem is established.For the problem of location selection of agricultural product logistics distribution center,the internal and external factors affecting the location decision are analyzed,and the principles of location selection,the relevant constraints of location selection,the processes of location selection,and the data required for location selection are introduced.The location model of the agricultural product logistics distribution center was established.In addition,the background and algorithm flow of particle swarm optimization algorithm are introduced,the principle of particle swarm optimization algorithm and its parameter selection strategy are fully explained.Aiming at the shortcomings of particle swarm optimization algorithm which is easy to fall into local optimal solution,the idea of hill climbing algorithm is merged with particle swarm optimization algorithm,and a hybrid particle swarm optimization algorithm is proposed.The hybrid particle swarm optimization algorithm was applied to the problem of agricultural product distribution vehicle scheduling optimization and distribution center location,and an example analysis was carried out.The result shows that the hybrid particle swarm optimization algorithm has a significant improvement in the success rate of search in dealing with complex combinatorial optimization problems,and it is a better processing method.At the same time,in order to make a better comparison,the basic particle swarm optimization algorithm and genetic algorithm are also applied to the problem of agricultural product distribution vehicle scheduling optimization and distribution center location.The test results and pros and cons of performance of the hybrid particle swarm optimization algorithm,the basic particle swarm optimization algorithm and the genetic algorithm are compared and analyzed.The experimental results show that the hybrid particle swarm optimization algorithm has stronger ability to jump out of the local optimal solution,and it can effectively avoid falling into the local optimal solution,and it also has a certain improvement in convergence speed and convergence precision.However,due to the mountain climbing operation,the running time has increased.