Rebalancing in Vehicle-sharing Systems with Service Availability Guarantees

A station-based vehicle sharing system consists of a fleet of vehicles (usually bikes or cars) that can be rented at one station and returned at another station. We study how to achieve guaranteed service availability in such systems. Specifically, we are interested in determining a) the fleet size and b) a vehicle rebalancing policy that guarantees that a) every customer will find an available vehicle at the origin station and b) the customer will find a free parking spot at the destination station. We model the evolution of the number of vehicles at each station as a stochastic process. The proposed rebalancing strategy iteratively solves a chance-constrained optimization problem to find a rebalancing schedule that ensures that no service failures will occur in the future with a given level of confidence. We show that such a chance-constrained optimization problem can be converted into a linear program and efficiently solved. As a case study, we apply the proposed method to control a simulated bike-sharing system in Boston using real-world historical demand. Our results demonstrate that our method can indeed ensure the desired level of service availability even when the demand does not fully conform to the assumptions of the underlying stochastic model. Moreover, compared with a state-of-the art rebalancing method, the proposed method can achieve nearly full service availability while making less than half of the rebalancing trips.