## 2 Literature Overview
### Location Planning of Charging Stations for Electric Buses  
At the present time, only few publications deal with location planning of charging stations for BEBs in public transport. [PERSON] et al. [2] present a mixed integer linear optimization model for determining locations for charging stations for a bus route. The model is based on a set covering problem. The objective is to minimize the number of charging stations needed. The authors consider constraints imposed by the buses' operation and the battery charging process. In addition, different energy consumption scenarios are considered to reflect external influencing factors on the buses' energy consumption, such as traffic volume and weather conditions. Standard optimization libraries are used for solving the problem. [PERSON] et al. [24] propose a mixed integer linear program in order to determine optimal locations of charging stations for the electrification of a single bus line in Mannheim. The problem is solved by using standard optimization libraries. Furthermore, partial charging procedures and battery aging effects over several time periods are considered. Since the problem is very complex, the solution approach is not suitable for larger instances. [PERSON] et al. [25] develop a dynamic optimization model to establish a charging infrastructure for BEBs in Stockholm, Sweden, considering restricted waiting times at intermediate stops on service trips given by the schedule and different currents of the charging systems imposed by local conditions. They provide statements about the application possibilities of BEBs in urban areas and effects on vehicle rotations. Within both works, no line changes of the buses used are considered. [PERSON] et al. [26] consider energy consumption uncertainties within location planning of charging stations for BEBs in public transport. Therefore, the authors propose a robust optimization model represented by a mixed integer linear program. Using real-world data, the authors show that the proposed solution model can provide optimal locations for charging stations that are robust against uncertain energy consumption of BEBs. [PERSON] et al. [27] introduce a spatial-temporal model for a large-scale planning of charging-stations for BEBs in public transport. The authors consider characteristics of BEBs operation and plug-in fast charging technologies. The model is represented by a mixed-integer second-order cone programming formulation with high computational efficiency. A case study using data from Shenzhen, China is used to analyse the robustness of the solution model to timetable changes. Based on the solution method presented in this paper, [PERSON] et al. [13] present an exact mathematical model for integrated optimization of vehicle scheduling with BEBs and location planning for charging stations. The authors particularly perform a robustness analysis and study the impact of technological aspects such as battery capacity, charging power, and energy consumption as well as economic issues containing investment costs for charging stations and electric buses. A computational study points out that the exact solution model introduced is not capable of solving realistic problem instances to optimality. Regarding related optimization problems in the scope of transportation, there are some contributions dealing with the charging infrastructure for electric vehicles. RegardingVehicle Routing Problems_ (VRP) with electric vehicles, [PERSON] et al. [28] propose a solution approach for the simultaneous determination of optimal locations for charging stations and vehicle routes. They show that this approach leads to lower total costs of the vehicle deployment by comparison to locations of charging stations known a priori. [PERSON] and [PERSON] [29] also deal with the simultaneous determination of locations for charging stations and routes for electric vehicles. The authors extend this optimization problem by considering uncertain characteristics of the customers to be served.