Research On Optimization Model And Algorithm Of Urban Logistics Distribution Route Under Uncertain Environment

In recent years,the rapid development of our country's logistics industry has made it a very important place in country's tertiary industry.The development of the logistics industry has attracted more and more attention.As a branch of the logistics industry,urban logistics affects the quality of life of urban people.Due to the small scope of activities of urban logistics and the large and scattered number of customers,it is greatly affected by uncertain factors such as traffic congestion and changes in customer demand.How to deal with the uncertain factors in the process of urban logistics and distribution reasonably,enable enterprises to reduce distribution costs while reducing carbon emissions,and improve customer satisfaction is a major challenge facing urban logistics.The thesis first conducts an in-depth study of the existing research results on the vehicle routing problem,summarizes the deficiencies in the existing research,and concludes the necessity and importance of studying the problem of urban logistics distribution route optimization under uncertain environments.According to the actual situation and the existing research results,the uncertainty factors faced in urban logistics distribution are summarized as customer demand,vehicle travel time,vehicle unit distance fuel consumption,vehicle unit distance distribution cost,etc.In order to accurately grasp the biggest uncertainty factor of the travel time of distribution vehicles,the paper introduces the traffic congestion delay coefficient,and considers the three aspects of road conditions,traffic conditions and traffic control,and establishes an evaluation index system for the traffic congestion delay coefficient.The fuzzy analytic hierarchy process based on expert weights is used to evaluate the traffic congestion delay coefficient of the road,which provides effective help for calculating the actual travel time of the distribution vehicle.The purpose of using expert weights in this method is to reduce the impact of the differences caused by the differences in expert professions,preferences,and hours of practice.The blur is mainly reflected in the scoring method of experts.When scoring,experts first select an interval and then give it within the interval A certain number,thus forming a fuzzy triangular number,reducing the impact of information uncertainty and subjectivity of experts.On the basis of considering the traffic congestion delay coefficient,the uncertain factors such as the fuzzy time window of the customer,the uncertain customer and the fuzzy fuel consumption per unit distance are considered.First,based on the dynamic customer's product consumption and the latest order time,determine whether to include it in the distribution range,and then consider the distribution vehicle number,customer satisfaction and vehicle fuel consumption and other goals,and establish an urban logistics distribution routing problem opportunity constraint planning model And,based on the research results,transform the chance-constrained programming model into an equivalent deterministic model.In the non-dominated sorting genetic algorithm with elite strategy,two operators,repair and education,are added to improve the convergence speed of the algorithm,which is used to solve the multi-objective model established in the paper.At the end of the paper,the breakfast delivery of a company in Lanzhou was taken as an example.According to the model established in the paper,MATLAB software was used to adopt genetic algorithm,non-dominated sorting genetic algorithm and improved undominated sorting genetic algorithm to solve the case,and the results of the three algorithms are compared and analyzed.The convergence rate of the three algorithms and the distribution of Pareto frontier in the solution are analyzed.