Research On Multi-Objective Optimization Of Bike-Sharing Rebalancing Problem

Under the background of "dual carbon",the zero emission shared bicycle has become the first choice for many users to travel.With its ease of using and parking flexibility,it not only solves the problem of urban short distance travel,but also helps to reduce urban traffic jams.However,the one-way and tidal nature of bike sharing users’ travel often leads to the mismatch between supply and demand of no car available and no place to park,which increases the operating costs of enterprises and reduces user satisfaction,greatly hindering the healthy development of bike sharing industry.Therefore,how to effectively rematch the supply and demand of shared bicycles,that is,the rebalancing of bicycles is very important.In this paper,we study the multi-objective bike-sharing rebalancing problem under the background of mixed fleets,in which the mixed fleets consist of fuel vehicles and electric vehicles.Firstly,the multi-objective mixed integer programming model is established with the minimum total cost of rebalancing and the minimum of unsatisfied rebalancing demand as two optimization objectives,and an improved multi-objective particle swarm optimization algorithm is designed to solve the model.The crossover operator of genetic algorithm is introduced to improve the convergence of the algorithm,and the local optimization is skipped through the variable neighborhood search mechanism.The simulation test results of the model shows that the decision-maker can choose a reasonable rebalancing scheme from the Pareto solution set obtained according to his own preference.In addition,in order to verify the good and efficient solution performance of the algorithm,it is horizontally compared with the standard MOPSO algorithm and NSGA-II algorithm.The simulation test results show that the algorithm has strong advantages in the quality of Pareto optimal solution set,distribution and convergence.Secondly,on the basis of the above research results,the recycling factors of broken sharing bicycles under uncertainty are considered.Due to quality problems or users’ riding habits,bicycles can not be ridden normally due to various failures,which will not only affect user satisfaction,but also occupy valuable parking space.Therefore,in view of the uncertainty of the recycling quantity of broken bikes,this paper introduces the budget uncertainty set to characterize the uncertainty variables,establishes a multi-objective robust optimization model with the minimum total travelling distance and the minimum unsatisfied rebalancing demand as the optimization objectives,derives a robust equivalent model,and then ε constraint method is used for accurate solution.Then the sensitivity analysis of robust control parameters and disturbance coefficients is carried out.The results show that the pareto solution set is more conservative with the increasing of robust control parameters and disturbance coefficients.Decision makers can make scientific and reasonable decisions by setting different robust control parameters and disturbance coefficients according to their own risk preference and target preference.