Stochastic optimization of electric microtransit scheduling with platooning

Electric microtransit system–equipped with platooning capabilities via advanced automation and communication technology–offers opportunities to tackle the resource allocation challenges of traditional bus system in face of fluctuating passenger demand. In this paper, we introduce a novel vehicle scheduling framework in which microtransit units can be dynamically coupled and detached to accommodate imbalanced trip demands through flexible capacity adjustment. We propose the electric microtransit scheduling with platooning problem (EMSP), which jointly optimizes trip chains and charging plans for individual microtransit units, as well as platoon formation and departure time for each trip, while explicitly accounting for traffic state stochasticity. The problem is formulated as a two-stage stochastic program that optimizes vehicle assignment, trip sequencing, and charging planning in the first stage, and incorporates departure shifting and charging-duration adjustment as recourse decisions in response to travel time and energy consumption uncertainty. An exact solution methodology based on a combination of branch-and-price and L-shaped algorithms is developed to solve the problem, with additional acceleration strategies achieved through the integration of both decomposition techniques. Computational experiments demonstrate the scalability and efficiency of the proposed approach, as well as its robustness relative to deterministic scheduling. Using real-world data from Wuxi, the results indicate that the proposed EMSP framework with flexible operating modes significantly reduces total system cost and improves resource utilization compared to conventional transit, particularly under low-demand scenarios. Moreover, higher-capacity microtransit vehicles–provided that their capacity is not excessively redundant relative to demand–tend to achieve lower total cost due to economies of scale.