Target-oriented distributionally robust optimization for battery swapping in shared micromobility systems

The imperative to mitigate global warming and reduce greenhouse gas (GHG) emissions has expedited the adoption of shared electric micromobility vehicles and battery swapping in urban transportation systems. This study examines a shared electric micromobility system and proposes a two-stage distributionally robust optimization (DRO) model to assist operators in optimizing battery swapping planning and operations under uncertain battery-swapping demands. To address the budget limitation, we introduce a CVaR-based satisficing index for cost control, facilitating robust target-oriented decision-making. To ensure practical implementation, we further reformulate this model into a tractable form that can be efficiently solved using off-the-shelf solvers. Numerical results derived from real-world data validate the effectiveness of our approach in maintaining total costs within specified budget limits, even under varying levels of uncertainty. Furthermore, the proposed model efficiently manages swapper travel distances for battery swapping while ensuring high service levels across the system, thereby enhancing both efficiency and sustainability. We also conduct numerous numerical experiments to evaluate the reliability of our proposed model by testing it across various parameters. We find that under the three-peak demand pattern, the battery allocation is lower while achieving a higher service level than those obtained under the single-peak pattern.