Distributionally robust chance-constrained vehicle scheduling with uncertain demand

With the rapid development of the new energy vehicle sharing system, the imbalance problem became increasingly prominent due to car returning in different sites. We consider uncertain demand. Based on mean and variance of demand distribution, we propose distributionally robust chance constraint vehicle scheduling model to minimize the chance constraints of the worst available vehicle and free parking spaces, then this model can be formulated as tractable counterparts. Finally, we apply this approach to the case of service with real operations data in Beijing. Numerical results show that, with the increase of service level, the scheduling cost and the number of vehicle increase obviously. When the service level improves to a certain extent, the number of vehicles and parking spaces should increase to meet the requirements of the service level.