Vehicle Relocation Triggering Thresholds Determination in Electric Carsharing System under Stochastic Demand

Abstract

Vehicle unbalance usually occurs in multistation electric carsharing systems. Threshold triggering method is one of the most practicable approaches for vehicle relocation, while determination of thresholds has not been sufficiently studied particularly for electric carsharing system. This paper presents an approach on determining the thresholds considering the stochastic demands and system states. Firstly, we establish a state transition model involving the stochastic variables to capture the dynamics of the number and battery status of vehicles as well as the traffic demands. Consequently, a dual-objective optimization model was developed to determine the proper values of thresholds. The solution algorithm employed the min-max robust optimization to tackle the uncertainty and the Pareto optimum to decide the solution under dual objectives. To test the distribution stochastic variables, we involve the orders data and the supplementary user survey. Comparison is conducted among three methods: The empirical rules, the deterministic method, and the stochastic method, where the results suggest that the stochastic method achieves better solutions on the dual objectives under stochastic demands.