Optimization of Bike-Sharing Scheduling Based on Markov Process for Weather Prediction
To address the imbalanced resource allocation of shared bikes in a university town,a single-objective mathematical planning model related to scheduling cost and user satisfaction is established to analyze the existing bike-sharing scheduling strategy and make improvement solutions.The problems in the process of bike-sharing scheduling are analyzed through questionnaires,and Python is used to obtain the relevant bike data.Based on this,weather conditions are predicted using the homogeneous Markov process,future changes in the number of bikes in the region are predicted using LSTM,optimal scheduling paths and scheduling quantities are solved using genetic algorithms,and the optimization solutions are proposed.The feasibility of the solutions proposed in this paper is verified by a sensitivity analysis.The results show that the optimized scheduling solutions shorten the single scheduling time and improve the user satisfaction,which is instructive for the management of shared bikes.
万方数据
维普
