A multi-objective approach for coordinating railway transport and electric demand-oriented services considering opportunity charging

Electrification is reshaping Mobility-on-Demand (MoD), yet coordinating electric demand-oriented shuttles with public transport remains challenging due to the interaction of routing, charging, and timetable decisions. This study introduces an Electric Vehicle Routing and Public Transport Rescheduling model (EVRP–PTR) that jointly assigns electric shuttle feeder services to passenger requests, schedules opportunity charging through in-network pantographs while maintaining time continuity in the charging process, and reschedules public transport departures to improve transfer synchronization. The problem is bi-objective, minimizing passenger door-to-public transport travel time and shuttle operating costs while accounting for travel-time uncertainty. Initially formulated as a mixed-integer nonlinear program (MINLP), the model is reformulated as a mixed-integer linear program (MILP), enabling the computation of globally optimal solutions. Due to the multi-objective nature of the problem, the Pareto front is obtained using the ϵ-constraint method. A case study in Athens, Greece, where electric shuttles feed the Athens–Thessaloniki railway corridor with five pantograph locations, shows that modest fleet increases substantially reduce passenger travel times and eliminate the need for en-route charging in some Pareto-optimal solutions. Under travel-time uncertainty, service-performance gains become less pronounced, and larger on-demand fleets are required to maintain comparable service quality. The proposed framework remains computationally tractable for mid-sized networks and can support tactical planning and opportunity-charging scheduling by quantifying trade-offs between service quality and fleet resources in integrated PT–EMoD systems.