## 2 Literature Review  
To the best of our knowledge, early contributions on the EVRP have been examined as an extension of the problem called the GVRP Green Vehicle Routing Problem introduced by [10]. This approach is a variation of the well-known Vehicle Routing Problem VRP, considering that a set of alternative fuel vehicles with limited autonomy needs to recharge at alternative fuel stations along the execution of their duties. Restrictions account for the maximum duration of the route, and a fixed service time is considered for refueling operation and customer visit. GVRP can be considered as the baseline for the EVRP. Several works have been published in the same research pipeline. In [11], the EVRP is described as a modified and comprehensive version of the VRP using Electric Vehicles, being an NP-Hard problem that requires substantial computational effort even for medium scale instances. In this sense, the authors in [12] are one step further in the GVRP, presenting a methodology to introduce refueling stations along the GVRP routes, with outstanding outcomes related with the updating part of the best-known solutions in the standard benchmark cases of the problem. Furthermore, in [13], the routing problem with EVs is studied taking into consideration more realistic elements, such as partial recharges and the different charging technologies. For the sake of the convenience, the literature review is presented in chronological order, considering works published in different academic and scientific databases. The EVRP is a relatively new problem in the specialized literature with a starting point presented in the GVRP, firstly introduced by [10] in 2012, as a generic of green logistics problem. Thereafter, an improved approach with electric and fuel-engined vehicles is found in [14], encompassing the original and reduced graph where the battery power consumption is included along the edges. Later, in 2015, time windows' constraints and the use of charging stations are incorporated in the EVRP by the authors of [15], allowing partial battery recharge and seeking to reduce charging time along the route. In the next year (2016), a similar contribution is presented in [16], formulating the problem as a mixed integer linear programming model, defining a decision variable that reflects the battery state of charge. The problem is solved using the Adaptive Large Neighborhood Search Algorithm ALNS. Beyond the traditional contributions, the decision-making analysis proposed by the authors in [17] reveals the influence of different factors in the charging station location for EVs, such as service availability, engineering feasibility, and economic, social, environmental, and land factors. Subsequently, in 2017, various works for EVRP are found in relation with time windows and heterogeneous fleet [18; 19]. The power distribution system is incorporated, considering grid ancillary services with Vehicle to Grid, whilst warranting optimal routing. Nonlinear charging is also introduced in the same period, providing a closer approach to the charging process that could affect the EV routing time [20]. Furthermore, routing efficiency is an additional feature mentioned in [21] for the transportation electrification. The research deals with a data-driven method for EV energy consumption prediction based on external disturbances. Similar to the work presented in [17], in [22], a decision-making framework suitable for investors is performed, providing the optimal location of charging facilities in residential areas. Through the papers analyzed for 2018, in [23], the time operation is minimized on a fleet of electric vans, taking into account that energy required to go through an arc of the transportation network may be influenced not only by the distance, but also by the current cargo load. Similar contributions can be identified from the stochastic perspective in [24],defining random features for the energy cost of each road segment, due to environmental factors.