Before revision:  This leads to an "online" charging scheduling problem that is  formulated as a multistage stochastic programming (MSP) problem in this paper. Instead of solving the MSP using the standard approaches such as sample average approximation (SAA), we show that solving a deterministic counterpart of the stochastic problem yields significant complexity reduction with negligible performance loss. Compared with the SAA approach, the computational complexity of the proposed algorithm is drastically reduced from O(\epsilon^T) to O( T^3) per time stage , where T is the total number of the time stagesand \epsilon is a constant related to the accuracy of the solution.
After revision:  This leads to an "online" charging scheduling problem that is formulated as a finite-horizon dynamic programming with continuous state space and action space. To avoid the prohibitively high complexity of solving such a dynamic programming problem, we provide a Model Predictive Control (MPC) based algorithm with computational complexity O( T^3) per time stage , where T is the total number of the time stagesand \epsilon is a constant related to the accuracy of the solution.