Study On The Bike Rebalancing And Recycling Problem

In recent years,in order to solve the problems of environmental pollution and traffic congestion,the bike-sharing market has sprung up and developed rapidly under the background of the rapid development of the Internet economy and the growing sharing economy,which greatly alleviating the "last kilometer" of urban traffic problems.As a short distance transportation tool,shared bikes have been favored by users at home and abroad.As a result,bike-sharing manufacturers have entered the bike-sharing market one after another.On the one hand,it provides convenience for our social life.On the other hand,due to the excessive investment in bicycles and the lack of relevant regulatory policies issued by government departments,the development of shared bikes has gradually exposed a large number of problems.For example,"tidal" phenomena caused by supply and demand imbalances between sites;The phenomenon of "piling up" caused by the failure of the broken down car to be parked and placed indiscriminate and not recovered in time;Due to the mismatch between supply and demand among different stations,the scheduling of shared bikes is unreasonable.In the previous researches on the rebalance of shared bikes,only the single-target scheduling of shared bikes available between different stations or the recovery of broken cars are considered.No scholars have considered the multi-objective rebalance of shared bikes with broken cars.Based on the above background,this paper studies the Bike Rebalancing and Recycling Problem(BRRP).The following contents are studied: firstly,the composition of the shared bike system and the development status of the shared bike market are introduced,and the main problems existing in the process of the shared bike rebalancing and the shared bike recycling as well as their causes are introduced.Secondly,a multi-objective mixed integer programming mathematical model was established as the minimum value of the operating cost and unmet demand,and a multi-objective particle swarm optimization algorithm(BAS-MOPSO)was designed to solve the model.This algorithm takes into account both the excellent global search ability of longicaurus bead search algorithm and the precise optimization ability of multi-objective particle swarm optimization algorithm,which can make the particles caught in the local extreme value jump,and use the jumping particles as a new information source to make other particles learn again,so as to improve the search accuracy and search performance of the algorithm.Finally,taking the distribution network data of M Company,a bike-sharing operation enterprise,as an example,the test results of different scale examples show that the proposed BAS-MOPSO algorithm is superior to the multi-objective particle swarm optimization algorithm.On this basis,by comparing the test results of a number of multi-objective problems,it is proved that the BRRP problem proposed in this paper can effectively reduce the operating costs of bike-sharing enterprises and improve user satisfaction.The sensitivity analysis results further demonstrate the effectiveness and practicability of the BAS-MOPSO algorithm,which can obtain a higher quality Pareto optimal solution.