## 2 Literature Review
### The Location Routing Problem  
Perhaps the first work related to the location routing problem is the one by [PERSON] [25], who analyze the influence of transportation costs on location decisions. Moreover, [PERSON] and [PERSON] [1] quantify for the first time the benefits of considering routing decisions when locating facilities. They also state that solving each subproblem (location and routing) independently does not provide optimal solutions. Multiple variantsof the LRP have been proposed over time. These variants depend on the characteristics of depots (capacitated or not), vehicles (capacitated or not, homogeneous or heterogeneous fleet), costs (symmetric or asymmetric) or the consideration of uncertain parameters. All capacitated variants addressed by these authors assume that depot sizes are fixed and cannot be changed. Considering the limited computational power available at that time, the initial works on the LRP firstly solved the underlying location problem and used the obtained solution as a starting point to handle the corresponding routing problem. However, as the computational power has notably increased in recent years, the newest approaches deal with the LRP in an integrated manner [26; 27]. Among the recently published works on the deterministic LRP, [PERSON] et al. [28] propose a granular tabu search within a VNS framework to speed up computational times without decreasing the solutions quality. A biased-randomization-based metaheuristic of two phases is developed by [PERSON] et al. [27] to solve the capacitated version of the problem. [PERSON] and [PERSON] [29] propose a GRASP with a novel technique used to create clusters around the open depots. Traditional applications of the LRP include horizontal cooperation [30], electric vehicle routing problems [31; 32; 33], city logistics [34], humanitarian logistics [35] or supply chain network design [36]. Moreover, most recent applications are related to environmental issues [37], cold supply chains [38] or waste management [39]. When dealing with uncertainty, most works have focused on the use of stochastic modeling. One of the utilized approaches has been the hybridization of simulation techniques with metaheuristics. For instance, [PERSON] et al. [9] propose a simheuristic to solve an LRP with stochastic demands, by hybridizing Monte Carlo simulation with an iterated local search metaheuristic. A similar approach is employed by [PERSON] et al. [13], who address an LRP where the sizes of facilities to locate are also a variable to consider. [PERSON] et al. [10] also propose a simheuristic approach that combines a nondominated sorting genetic algorithm-II (NSGA-II) and Monte Carlo simulation. They tackle a multiobjective multiperiod LRP in the context of the hazardous waste management industry. Both generated waste and number of people at risk are stochastic. Inventory decisions are also taken into account. [PERSON] et al. [11] address a real-world case from an express delivery company in Shanghai. These authors tackle an LRP in which demand is stochastic and can be split for self-pickup. Then a simulation-based optimization model is proposed and two heuristics results are compared. Other parameters are also considered to be uncertain. For instance, [PERSON] et al. [14] hybridize an ant colony optimization metaheuristic with discrete-event simulation to solve an LRP in which both transportation costs and vehicle travel speeds are considered stochastic. Authors demonstrate that their proposed approach is not only efficient but is able to find statistical interactions among the different parameters. [PERSON] et al. [15] present an approach that hybridizes a genetic algorithm with simulation to solve a sustainable multiobjective LRP in the context of emergency logistics. The authors consider the travel distance, the demand and the cost of opening a depot as uncertain variables. Additionally, the emergence of new technologies introduces new challenges. This is the case of [PERSON] et al.