Study On Vehicle Scheduling Problem Model And Its Swarm Optimization Application Research In Logistics Network System

With the development of economic globalization,modern logistics has drawn more and more attention from all walks of life.How to optimize the combination of cargo resources and transportation resources based on the limited logistics facilities,logistics equipment and transport vehicles and integrated vehicle transportation process is an important issue in logistics management practice.This dissertation focuses on the multi-depot vehicle scheduling model and its optimization,logistics transportation time control model and its optimization,logistics network system multiple facilities planning,the improvement of BPR(U.S.Bureau of Public Roads,BPR)Road resistance function reflecting the degree of traffic congestion,and other issues in logistics network system.The main research contents are as follows:(1)In order to optimize goods and vehicle resources,multi-depot vehicle scheduling is proposed.In this vehicle scheduling model,logistics nodes can be accessed multiple times,the goods can be transhipped,the transit vehicles can participate in the scheduling and the vehicle load fluctuates.In this paper,a multi-depot vehicle scheduling model is established.(2)The article presents the improved ant colony algorithm,ant colony and particle swarm hybrid algorithm for the vehicle scheduling model with multi-depot points.In the process of model optimization,all vehicles search for feasible routes for all goods,and achieve multi-depot point access,which is easy to achieve global optimization.These optimizations reduced the local solution phenomenon which happened by dividing multi-depot problem directly or indirectly into multiple single depot vehicle scheduling problem.(3)A deterministic model of logistics transportation time control is established.The enumeration method and genetic algorithm are respectively used to solve the model,and the calculation results are compared and analyzed.On this basis,from the perspective of uncertainty,the uncertainty model of logistics transportation time control is established considering the uncertainty of transportation time.An improved genetic algorithm is designed to solve the model.(4)In order to meet the needs of customers,a planning model including location,scale and quantity of various facilities such as factories,logistics centers or distribution centers is constructed and solved by the particle swarm optimization algorithm.(5)When BPR function is used to calculate the impedance of traffic section,it is difficult to reflect that the traffic volume increases first and then decreases in traffic flow from unobstructed to crowded.In response to this problem,two improved functions that can objectively reflect the actual traffic conditions are proposed.