Currently, there is serious political support for the decarbonization of transport locally, nationally and even internationally. Public transport operators are focusing on the use of electric buses as an opportunity to reduce greenhouse gas emissions and improve air quality. However, using electric buses requires a functional infrastructure of urban charging points. Fast-charging can be made available thanks to the progress made on the major technological charging devices in recent years. In this study, we consider an optimization problem of the design of an infrastructure for a fast-charge city electric bus service. The decisions which have to be made include determining a mixed fleet of conventional and electric buses, points for electric chargers and power stations, quantities of charging plug devices, a distribution of electric buses between the routes, and matching chargers with power stations. The objective is to maximize the route-weighted total passenger capacity of electric buses. An Integer Linear Programming model has been developed to complement the existing non-linear model. The new model is efficient if the number of possible charging spots is small, which is natural and frequent in practice. Extensive computer experiments demonstrate that our approach delivers near-optimal solutions of the studied problem in ten minutes for real-world instances on a standard PC and it outperforms the earlier approach on every instance.

目前，在地方、国家乃至国际上，交通脱碳得到了认真的政治支持。公共交通运营商将电动公交车的使用作为减少温室气体排放和改善空气质量的机会。然而，使用电动公交车需要城市充电站的功能性基础设施。得益于近年来主要技术充电设备的进步，可以实现快速充电。在这项研究中，我们考虑了快速充电城市电动公交服务基础设施设计的优化问题。必须做出的决定包括确定传统和电动公交车的混合车队、充电器和发电站的点数、充电插头设备的数量、电动公交车在线路间的分布，充电桩与电站配套。目标是最大化电动公交车的路线加权总载客量。已经开发了整数线性规划模型来补充现有的非线性模型。如果可能的充电点数量少，新模型是高效的，这在实践中是自然而频繁的。广泛的计算机实验表明，我们的方法在十分钟内为标准 PC 上的真实实例提供了所研究问题的近乎最优的解决方案，并且它在每个实例上都优于早期的方法。已经开发了整数线性规划模型来补充现有的非线性模型。如果可能的充电点数量少，新模型是高效的，这在实践中是自然而频繁的。广泛的计算机实验表明，我们的方法在十分钟内为标准 PC 上的真实实例提供了所研究问题的近乎最优的解决方案，并且它在每个实例上都优于早期的方法。已经开发了整数线性规划模型来补充现有的非线性模型。如果可能的充电点数量少，新模型是高效的，这在实践中是自然而频繁的。广泛的计算机实验表明，我们的方法在十分钟内为标准 PC 上的真实实例提供了所研究问题的近乎最优的解决方案，并且它在每个实例上都优于早期的方法。