论文标题
DDKSP：一个数据驱动的随机编程框架，用于汽车共享重新定位问题
DDKSP: A Data-Driven Stochastic Programming Framework for Car-Sharing Relocation Problem
论文作者
论文摘要
汽车共享问题是共享经济的流行研究领域。在本文中，我们调查了不确定需求的汽车共享搬迁问题（CSRP）。通常，真正的客户需求遵循复杂的概率分布，这无法通过参数方法描述。为了克服这个问题，提出了一个称为数据驱动的内核随机编程（DDKSP）的创新框架，该框架集成了非参数方法 - 内核密度估计（KDE）和两阶段随机编程（SP）模型。具体而言，概率分布是由KDE衍生出的，KDE用作SP的输入不确定参数。另外，CSRP被配制为两阶段的SP模型。同时，引入了一种称为样品平均近似（SAA）和弯曲器分解算法的蒙特卡洛方法，以解决大规模优化模型。最后，基于纽约出租车旅行数据集的数值实验验证表明，所提出的框架的表现优于包括高斯，拉普拉斯和泊松分布在内的纯参数方法，分别在总利润方面分别为3.72％，4.58％和11％。
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.
