## 2 Literature Overview
### Scheduling Electric Buses  
As one of the first contributions dealing with alternative engine types within vehicle scheduling, [PERSON] and [PERSON] [14] present a solution method for the VSP taking into account different engine options. The solution approach is based on integer programming. Engines powered by compressed natural gas (CNG) are considered besides combustion engines. The approach aims at reducing emission levels within vehicle scheduling. [PERSON] et al. [15] consider a mixed fleet of vehicles consisting of electrically powered buses and buses without range limitations within the basic VSP. The authors apply a time-space network based exact solution method for the VSP introduced by [PERSON] et al. [16] to solve the enhanced optimization problem. Solutions obtained to this problem contain optimal flow values through the network. Therefore, strategies for flow decomposition are necessary to obtain vehicle rotations. The authors analyze six strategies for flow decomposition that aim at maximizing the proportion of feasible vehicle rotations for BEBs. Battery charging is assumed to be performed within constant time periods. The authors show that a simple substitution of traditional buses with BEBs leads to widely infeasible vehicle schedules. [PERSON] and [PERSON] [17] consider a fleet consisting entirely of range restricted vehicles. They consider vehicle scheduling with route and time constraints in order to limit the lengths and durations of vehicle rotations. However, battery charging is not considered. The authors propose one exact and two heuristic solution models together with techniques for reducing the problem sizes in order to solve even larger-scale problem instances. [PERSON] and [PERSON] [18] consider battery swapping in addition to limited operating ranges of BEBs within the VSP. To solve the problem, a solution method based on a Non-dominated Sorting Genetic Algorithm (NSGA-II) is introduced. A case study based on real-world data taken from a project in Shanghai is performed to analyze the solution approach. [PERSON] [19] addresses vehicle scheduling of BEBs with either battery swapping or charging and presents a model for restricting the maximum route distance. Both fast charging and battery swapping are presumed to be performed within constant time windows, but the time for fast charging depends on the location. [PERSON] and [PERSON] [20] deal with scheduling of BEBs incorporating charging procedures at given charging stations located within the road network. To solve the problem, they present a column-generation approach. A heuristic method is presented to obtain necessary initial solution. The algorithm is based on a greedy algorithm and computes vehicle rotations under consideration of range limitations and charging. In this work, again full chargings of vehicle batteries are assumed. As one of the first authors, [PERSON] et al. [21] address the E-VSP with partial chargings. They present an exact solution method based on mixed integer programming and an adaptive large neighborhood search heuristic approach. The results demonstrate that the exact solution methods is only applicable to small problem instances. However, the heuristic solution approach also solves larger instances in a reasonable amount of time. [PERSON] et al. [22] also consider partial charging procedures of BEBs. The authors introduce a solution approach based on column generation. Charging times depend linearly on a battery's SoC. Furthermore, battery aging and time-dependent energy prices are considered. The authors show that in some cases, the consideration of partial charging procedures leads to cost savings. Recently, [PERSON] et al. [23] proposed an exact solution method for the E-VSP based on dynamic programming. Within this contribution, battery aging is particularly considered. The objective of the solution method is minimize the total costs especially incorporating costs for battery replacements during the life spans of the vehicles deployed.