Collaborative optimization model of fleet dynamic scheduling and supporting facility layout considering SAEV charging demand

To achieve the collaborative optimization of fleet scheduling and supporting facility layout of shared autonomous electric vehicle (SAEV), a multi-objective nonlinear programming model was established, with the objectives of minimizing the SAEV fleet size, total vehicle travel distance, total passenger travel time, and construction cost of charging and parking facilities. Based on a time-expanded network, the dynamic OD travel demand of passengers, the dynamic scheduling strategy of the SAEV fleet, and the spatiotemporal displacement of passenger flows were described. Furthermore, the capacity constraints of nodes and arcs in the network were utilized to characterize the congestion effects of charging and parking facilities in traffic zones and connecting roads, respectively. Distinguished from traditional models, several constraints were considered, including the charging demand of the SAEV fleet, dynamic conservation relations of charging and operating SAEV flows and passenger flows, ridesharing passenger number limit, and capacity limits of supporting facilities. To improve the solution efficiency of the model, linear approximation and linear equivalence techniques were employed to reconstruct the model into a mixed-integer linear programming model, and the Epsilon-constraint method was used to solve the Pareto-optimal solutions of the multi-objective model. The validity of the model was verified using the travel data of the road network in Chengdu, and a scenario comparison analysis was conducted for different numbers of ridesharing passengers and ratios of fleet charging demand. Research results show that when the number of ridesharing passengers increases from one to four, the total vehicle travel distance decreases by 77.87%; the fleet size decreases by 88.56%, and the construction cost of supporting facilities decreases by 96.80%, but the total passenger travel time increases by 125.46%, which indicates that operators should select an appropriate ridesharing strategy to ensure passenger travel efficiency. When the proportion of SAEV charging demand decreases from 30% to 5%, the total vehicle travel distance decreases by 10.77%, the operator fleet size decreases by 3.69%, and the construction cost of supporting facilities and total passenger travel time remain unchanged, which indicates that improving the SAEV endurance performance has great potential in improving the transport efficiency of SAEV fleet and reducing the operation cost of SAEV fleet.