Optimizing fleet, staff configuration and operational strategies in one-way mixed fleet carsharing systems: a Lagrangian relaxation-based approach

This study explores enhancing carsharing services by integrating gasoline and electric vehicles into a one-way mixed fleet carsharing system (OMFCS). The focus is on optimizing configurations (fleet and staff size, initial deployment) and operational strategies (vehicle relocation and staff rebalancing) while considering carbon emission costs. Employing a space-time-electricity network modeling approach, we developed an integer linear programming model to tackle the configurations and operational strategies optimization problem. For solving this model, we introduce a Lagrangian relaxation-branch bound approach, which integrates subgradient, dynamic programming and greedy-based heuristics algorithm. An illustrative case and a real-world case are conducted to demonstrate the efficiency of the proposed solution method and the analysis sheds light on the configurations and operational strategies of OMFCS. The sensitive analysis results suggest that OMFCS is more profitable and balances user service quality and carbon emissions better than carsharing systems using only one type of vehicle.