  Car-sharing issue is a popular research field in sharing economy. In this
paper, we investigate the car-sharing relocation problem (CSRP) under uncertain
demands. Normally, the real customer demands follow complicating probability
distribution which cannot be described by parametric approaches. In order to
overcome the problem, an innovative framework called Data-Driven Kernel
Stochastic Programming (DDKSP) that integrates a non-parametric approach -
kernel density estimation (KDE) and a two-stage stochastic programming (SP)
model is proposed. Specifically, the probability distributions are derived from
historical data by KDE, which are used as the input uncertain parameters for
SP. Additionally, the CSRP is formulated as a two-stage SP model. Meanwhile, a
Monte Carlo method called sample average approximation (SAA) and Benders
decomposition algorithm are introduced to solve the large-scale optimization
model. Finally, the numerical experimental validations which are based on New
York taxi trip data sets show that the proposed framework outperforms the pure
parametric approaches including Gaussian, Laplace and Poisson distributions
with 3.72% , 4.58% and 11% respectively in terms of overall profits.
