The deployment of electric vehicle (EV)–based feeder bus networks is increasingly promoted to support sustainable urban transportation systems. However, their operational planning is challenged by limited battery capacity, charging time requirements, and restricted charging infrastructure, which introduce complex trade-offs between operational efficiency, energy consumption, and service coverage. This study aims to develop a Multi-Objective Mixed-Integer Programming (MOMIP) model that explicitly incorporates battery state-of-charge dynamics and charging station constraints for optimizing electric feeder bus networks. The proposed model simultaneously minimizes operational costs and total energy consumption while maximizing service coverage, enabling a comprehensive evaluation of conflicting operational objectives. The use of MOMIP is justified by the need to capture Pareto-optimal trade-offs among these competing objectives within a unified mathematical formulation. Numerical experiments based on hypothetical operational scenarios demonstrate that the model generates feasible Pareto-optimal solutions, revealing clear trade-offs between cost efficiency, energy usage, and network accessibility. Analysis further indicates that increasing charging capacity significantly enhances system performance, reducing energy consumption by more than 20% and improving service coverage by over 7 percentage points. The proposed model provides a robust decision-support tool for transport planners and contributes to the development of energy-efficient and sustainable electric feeder bus operations.
Yanti et al. (Wed,) studied this question.