Research On Time-dependent And Multi-objective Vehicle Routing Problem With Time Windows Based On Non-dominated Sorting Genetic Algorithm

With the rapid development of mobile Internet in recent years,more and more customers are participating in online shopping,which has also brought new challenges to offline distribution.On the one hand,customers have higher requirements for the timeliness of logistics distribution services.For example,in the distribution of fresh agricultural products and catering,the products are easy to deteriorate,so there are higher requirements for the distribution time;On the other hand,with the increase in the number of customers,the optimization of the logistics distribution process needs to consider a variety of factors,such as operating costs and service quality.How to save costs as much as possible while satisfying the good service level of customers has become an urgent problem for logistics companies.The problem of urban logistics and distribution can be considered as a vehicle routing problem.Traditional vehicle routing problem regards vehicle traveling speed as a fixed value,and vehicle traveling time is only related to the distance.But in real life,vehicle traveling speed is largely affected by the traffic congestion on urban roads.For example,in the peak periods such as morning and evening,vehicle traveling speed will be significantly lower than the non-peak periods such as noon and night,so if the change of vehicle speed is considered in the logistics distribution process,the real situation can be better simulated.Based on the above reasons,this paper comprehensively considered the timeliness of logistics distribution and the congestion of urban road traffic,and proposes the time-dependent and multi-objective vehicle routing problem with time windows(TD-MO-VRPTW),and solved it based on related algorithms.Based on a thorough investigation of domestic and foreign research,this paper selects a suitable time-dependent model and constructs a multi-objective mixed integer linear programming model for the problem.Then two algorithms are used to solve the multi-objective optimization problem:NSGA-? and NSGA-?.Finally,an instance from Solomon benchmark set is selected for example analysis,and the results are compared with those obtained by single-objective optimization.The results show that the model and algorithm in this paper are reasonable and effective,which can be used for reference to other similar research problems.