A Robust Optimization Framework for eBus Charging Infrastructure Planning
提出一种迭代局部搜索算法，优化电动公交车充电站的数量和位置，通过引入备用充电站和灵活能量分配提升鲁棒性，在爱尔兰城市数据测试中优于现有方法。
Abstract Efficient and robust charging infrastructure plays a key role in accelerating the adoption of electric buses (eBuses) in urban transit systems. Unlike diesel buses, eBuses depend on strategically placed fast-charging stations to maintain continuous operation while ensuring high service quality. Planners must balance infrastructure investment, network reliability, and operational feasibility when determining optimal charging locations. Providing redundant charging access prevents disruptions from station failures, energy consumption fluctuations, and scheduling uncertainties. This work introduces an Iterated Local Search (ILS) algorithm that optimises the number and placement of charging stations. The approach improves robustness by incorporating backup charging stations and flexible energy redistribution techniques. We evaluate performance using real-world public transportation data from Cork and Dublin, comparing results against the state-of-the-art Large Neighborhood Search (LNS) method. Our experiments show that ILS outperforms LNS in 81.2% of cases by requiring fewer charging stations, whereas LNS performs better in 7.8% of cases. ILS is particularly effective in high-energy-demand scenarios with strict timetable constraints and realistic discharging rates, demonstrating its effectiveness in ensuring viable and scalable charging station placement.