Research On Improved Genetic Algorithm For Solving Vehicle Routing Proble

With the rapid development of network technology and the popularity of e-commerce,logistics and distribution business is also growing,making offline logistics and distribution face new challenges.Firstly,for many commodities,the timeliness of logistics and distribution is extremely strict,for example,fresh products often need to be delivered to customers in the shortest possible time.Secondly,as the customer base continues to expand,logistics delivery needs to take into account a variety of factors,such as cost control and service levels.Logistics companies are increasingly faced with the urgent question of how to provide consumers with a good experience while at the same time reducing delivery costs.In this context,it is very important to study logistics distribution routing optimization methods.Vehicle Routing Problem(VRP)is a typical NP-hard problem.In this paper,an improved genetic algorithm for the extended VRP problem is investigated,and the specific research work is developed as follows:Firstly,Vehicle Routing Problems with Time Windows(VRPTW)is studied,for the traditional genetic algorithm in solving the vehicle path problem with time window is easy to fall into local optimum,early convergence and other problems,a genetic algorithm based on adaptive large neighborhood search is proposed to solve VRPTW,mixing genetic algorithm with adaptive large neighborhood search algorithm to improve the local search ability of genetic algorithm,while optimizing the initialization strategy of the population,and then using the classical test case of VRPTW in Solomon database to conduct experiments,the experimental results show that hybrid genetic algorithm can effectively solve VRPTW and has certain reference value for vehicle routing problems.Secondly,the vehicle routing problem of cold chain logistics distribution is studied,and the customer satisfaction function is designed on the basis of VRPTW to establish a dual-objective problem model of minimizing total cost and maximizing satisfaction,which is more realistic and can be applied to realistic scenarios such as logistics transportation.Then,an improved multi-objective genetic algorithm is proposed to solve the problem.First,the generation of the initial population is improved so that the algorithm can generate a better initial population.Second,the dynamic disaster mechanism is added so that the algorithm can avoid falling into local optimal solutions to a certain extent.The experimental results show that the improved multi-objective genetic algorithm is effective in solving the vehicle routing problem of cold chain logistics distribution,which provides a certain reference for vehicle routing planning in cold chain logistics distribution.