Research On Vehicle Routing Problem With Time Windows Based On Improved Particle Swarm Optimization

Vehicle routing optimization problem has always been the research hotspot of domestic and foreign scholars.With the constant change of the actual scene,vehicle routing problem has derived many variants to solve more complex problems in practice.There are still has a high empty rate of freight transportation,waste distribution of transport resources and other problems in the field of freight transportation in our country.How to reduce transport costs,improve social benefits and economic benefits of enterprises is still a problem to be further researched.In addition,driven by the national energy conservation and emission reduction policies,logistics vehicles are constantly changing from the conventional vehicles to electric logistics vehicles.In the transition process,how to scientifically and reasonably dispatch the two types of vehicles is also an urgent problem for modern enterprises to solve.Aiming at the problem of high unloaded ratio for the freight vehicles with backhaul,which considering the constraints of customer service priority,time window,vehicle capacity,toll cost and fuel cost.The problem’s goal is to maximize profit and establish a mathematical model of the vehicle routing problem with backhaul.An improved strategy is introduced to the standard particle swarm optimization algorithm.Firstly,the initial population of the algorithm is sorted by fitness value and the randomly generated individuals replace half of the individuals with poor fitness value.Secondly,the speed updating formula of particle swarm optimization algorithm is improved by combining the principle of iterative update of gray wolf algorithm,which improves the efficiency and quality of the algorithm.By comparing the results of particle swarm optimization algorithm and LINGO,the scientificalness of the mathematical model and the effectiveness of the improved particle swarm optimization algorithm are verified.According to the requirements of low carbon and environmental protection in urban distribution,multi-depot distribution vehicle routing problem with fuel vehicle and electric vehicle as distribution vehicles is research topic of this paper.It also considers the time requirements of the customer.With the current actual situation,the conventional vehicles not going to be replaced completely.The goal is to minimize total distribution costs.The mathematical model of the mixed fleet vehicle routing problem is established.An improved particle swarm optimization algorithm is designed.In standard particle swarm optimization algorithm on the basis of application of the theory of good-point set generating initial population to increase the diversity of particle swarm optimization algorithm.In the particle swarm optimization algorithm optimization iteration phase,in order to achieve the global optimal and improve the quality of solution,the inertia weight and local search principle of the global-localneighborhood particle swarm optimization(FRS-PSO)is used to update the velocity formula,which effectively avoid the particle swarm optimization algorithm into local optimum.Finally,a numerical example is designed to test,which verified the effectiveness of the proposed improved particle swarm optimization algorithm and model.