Carsharing provides members access to a fleet of shared-use vehicles in a network of locations on a short term as-needed basis. It enables the individuals to gain the benefits of private vehicle use without the costs and responsibilities of ownership. This paper addresses the dynamic vehicle allocation problem in a car sharing context, i.e., a decision making problem for vehicle fleet management in both time and space in order to maximize profits for the car sharing service operator. A multistage stochastic linear integer model with recourse is formulated which can account for system uncertainties such as carsharing demand variation. A Monte Carlo sampling based stochastic optimization method is proposed to solve the carsharing dynamic vehicle allocation problem. Preliminary results are discussed and related insights are presented based upon a five-stage experimental network pilot study.
Abstract