Sequential Simulation Optimization with Censoring: An Application to Bike Sharing Systems

Sequential Simulation Optimization is an online optimization framework where an operator iterates periodically between collecting data from a real-world system, using stochastic simulation to approximate the optimal values of some operational variables, and setting some choice of variables in the system for the next period. The aim is to converge to an optimum efficiently, as uncertainty due to finite data and finitely many simulations eventually reduces. Using Bike Sharing Systems (BSS) as a motivating example, we analyze a variant where data from the real-world system is subject to censoring, whose nature depends on the system variables selected by the operator. In the BSS setting, censoring is of customer demand, to pick up or drop off bikes. We show that a method built upon Sample Average Approximation attains asymptotically vanishing error in its parameter estimates and specification of the optimal operational variables.