Research On Scheduling Optimization Of Sharing Bicyle

Public bicycles are an important part of the city's public transportation system.It is a green mode of transportation that saves energy and protects the environment.It also solves the difficult problem of last mile of residents' travel.Sharing bicycles a new model for public bicycle system.They are no longer limited to fixed lock piles and can be borrowed and returned in a prescribed free area,so they are loved by a large number of users.Shared bicycles develop very rapidly.After only a few years of development,it has spread all over the country,even going abroad.With the continuous release of shared bicycles,there have also been many management problems,such as market saturation,crowding public space,and affecting traffic order.Many cities have stopped companies to deliver sharing bicycles due to the confusion of shared bicycle management.The number of shared bicycles initially is crucial for the healthy operation of shared bicycles and urban development.After the shared bicycle is put into use,the frequent borrowing and returning behavior of users leads to an unbalanced distribution of the shared bicycle from time and space aspect,so that there are no bicycles to borrow in some areas,meanwhile other areas have idle bycicles.Reasonable scheduling optimization can make the shared bicycles distributed and balanced again,satisfying the user's borrowing demand and improving user satisfaction.Keep the shared bike system in a state of dynamic balance.If there is no effective scheduling,even more bicycles can not meet the needs of users.However,frequent scheduling requires a lot of cost,and it seems that it is unreasonable to satisfy the user's borrowing demand regardless of the cost.Therefore,the shared bicycle scheduling scheme needs to focus on how to balance the two factors.Therefore,the reasonable number of shared bicycles and timely and effective scheduling are crucial for shared bicycle health operations and urban development.Based on this,this paper mainly studies the forecast of the volume of shared bicycles in the region,and optimizes the shared bicycle scheduling scheme after the shared bicycles are put into the market.This paper firstly studies the relevant research contents of sharing bicycles and public bicycles at home and abroad,including the shared bicycle demand forecast methods,the shared bicycle scheduling optimization model and algorithm,and summarizes the previous research achievents and analyzes the differences between domestic and foreign research.Then it introduces the development history of public bicycles at home and abroad,the operation modes and shortcomings in each stage.This paper also introduce the differences between pile bicycles and shared bicycles,the current public bicycle scheduling mode and the principle of shared bicycle scheduling optimization.This paper uses the questionnaire method and the field observation method to count the number of people in the school,the residential area,and the subway(with a large amount of borrowing demand)and obtains.the proportion of the number of people who have borrowed the vehicle during the morning rush hour.This paper use the bicycles demand in morning rush and bicycle turnover rates to forecast the throws in quantityThen,the scheduling optimization study of the shared bicycle is carried out.Firstly,the scheduling demand of each station is obtained according to the borrowing rate and the arrival rate of each gathering area.Then,a multi-objective function including scheduling cost and user satisfaction is established,where the cost includes variable costs and fixed costs,the user satisfaction is represented by the unsatisfied scheduling demand.The scheduling cost and the user satisfaction are given the same weight,so that the multi-objective function is transformed into a single objective function to solve.This problem belongs to the NP-hard problem.Finally,the improved genetic algorithm is used to solve the problem.The matlab software coding is used to obtain the final results.Finally,the case study of Xianlin University City in Nanjing is used to prove the validity of the model.