Integrated Optimization of Planning and Operations for Shared Autonomous Electric Vehicle Systems

Shared autonomous electric vehicle systems will be a promising alternative for sustainable urban mobility. This study investigates an integrated optimization problem for shared autonomous electric vehicle systems that determines the long-term charging facility deployment at the planning level (e.g., the sizing and configurations of charging facilities), while the vehicle assignment, relocation, and charging decisions in the short term are also optimized at the operational level. A two-stage stochastic integer program is formulated to capture the demand uncertainty, in which an event-activity space-time-battery network is proposed for tracking the charging choices and battery states of vehicles and determining the optimal operational decisions. For dealing with a large number of scenarios in the stochastic program, the sample average approximation scheme is applied as the sampling strategy. An accelerated two-phase Benders decomposition-based algorithm is proposed for solving the two-stage program. The modeling and algorithm approach is tested on a large-scale case in Shanghai City in China. Extensive experiments show that the proposed approach can always find high-quality solutions in an efficient way. Numerical results indicate that deployment of both normal- and fast-charging infrastructure can increase the system profit and improve its operational performance.