An optimization framework for the time-dependent electric vehicle routing problem with shared mobility: A step toward smart cities

This paper introduces a mixed-integer linear programming (MILP) formulation for a time-dependent electric-vehicle dial-a-ride problem in shared mobility systems. The model optimizes vehicle routing and charging decisions under time-varying travel times that represent congestion conditions as exogenous inputs. The formulation incorporates operational features including stepwise time-dependent link travel times (peak/off-peak), nonlinear charging with partial recharging, heterogeneous charging infrastructures, queueing delays at charging stations, and passenger time-window constraints. The model is solved using CPLEX and demonstrated on a small illustrative instance to validate feasibility and quantify tradeoffs between shared and private travel patterns. In the case study, average vehicle mileage decreases from 56.42 km (private) to 46.83 km (shared), while average travel time increases modestly from 84.63 min/veh (private) to 89.32 min/veh (shared). These results suggest that, under the assumed demand and congestion inputs, shared electric mobility can reduce vehicle-kilometers traveled with limited additional travel time. The numerical study is intended as a proof-of-concept; scaling to realistic urban networks and incorporating dynamic demand or congestion feedback will require algorithmic enhancements and extended modeling frameworks.