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