Reducing Car-Sharing Relocation Cost through Non-Parametric Density Estimation and Stochastic Programming

In this paper, we present a data-driven stochastic programming model for reducing car-sharing relocation cost under uncertain customer demands. Instead of using parametric methods to estimate demand probability distributions, we propose an integration of non-parametric kernel density estimation, sample average approximation and a two-stage stochastic programming model. The proposed approach computes high quality car-sharing relocation solutions by better leveraging the information provided by large-scale historical data. To validate the performance of the proposed approach, we conduct numerical experiments using the New York taxi trip data sets. Our results show that the proposed approach outperforms the parametric approach using Laplace and Poisson distributions and the deterministic model in terms of profit and combined holding and relocation costs. Most importantly, it reduces on average more than 50% of relocation rate compared with the parametric method and 67% of relocation rate compared with the deterministic model.