Abstract:

In recent years, under the background of sharing economy and supply-side reform, vehicle sharing service becomes an innovative service model to mitigate the urban traffic congestion. However, in the process of vehicle sharing services operation, there are many uncertain factors which make the normal operation less efficient. To explore the approaches to solve this problem, this paper takes the electric vehicle as the main component of vehicle sharing service. In the initial stage of vehicle configuration, considering the partial moment information under the uncertain requirement, and combining the minimizing the worst-case theory, we propose distributionally robust vehicle allocation model. Further, partly considering three categories of situations namely "determined rental requirement", "stochastic rental requirement" and "partial uncertain rental requirement with known moment information", we also put forward corresponding vehicle sharing service vehicle configuration model, compare the results of the three models combining information of the 15 rental point in Beijing, and compare the corresponding allocation quantities and vehicle allocation cost through Monte Carlo simulation. The results show that the proposed method has better robustness relative to the other two models. At the same time, it can better reduce operating costs in an uncertain environment. It has strong practical application value for operators to make decisions on initial vehicle allocation through partial demand information.

Key words: vehicles allocation, distributionally robust optimization, demand uncertainty