## 2 Literature Review  
In city logistics, direct delivery might be challenged by unexpected traffic and unattended delivery. This condition was one of the motivations for the introduction of two-echelon VRP (2E-VRP). Many studies are comparing the effectiveness of the HD approach using a single echelon strategy modelled as CVRP model to the CP approach of using two-echelon strategy. The basic form of 2E-VRP is considered the two-echelon capacitated vehicle routing problem (2E-CVRP). The term was introduced by [PERSON] et al. [5]. The study proposed a mathematical model for the 2E-CVRP, which is based on the classical CVRP problem with the addition that the delivery from depot to customers is managed by routing and consolidating freight through intermediate depots. The nature of CVRP is an NP-Hard problem. Therefore, the 2E-CVRP as an extension of the CVRP is NP-hard [6]. Research conducted by [PERSON] et al. [7] shows that the basic method used is the branch and cut algorithm in non-mathematical mathematical models that provides a strict lower limit for 2E-CVRP symmetrical. The computational results show that our algorithm is superior to the algorithm previously proposed in the literature, and we can complete 47 of 93 tests as an example of optimality in which 34 of them are solved for optimality for the first time. [PERSON] et al. [8] developed Integer Programming formulation and two branch-and-price implementations for the Two-Echelon Capacitated Vehicle Routing Problem. One algorithm considers routes that satisfy the elementary conditions, while the other relaxes such constraints when pricing routes. [PERSON] et al. [9] developed 2E-VRP through the addition of side constraints that function to derive valid lower bounds and an exact method that decomposes the 2E-CVRP into a limited set of multi depot capacitated vehicle routing problems. This research has quite a good impact on the 2E-VRP research area because it is able to offer an exact algorithm that is proven capable of solving 2E-VRP problems from journal sources before 2013. The research's exact algorithm solved to optimality 144 out of the 153 instances from the literature and closed 97 of them for the first time. [PERSON] [10] in his paper presents a concept known as hybrid approach to modeling and optimization the 2E-VRP with integrating two environment of mathematical programing (MP) and constraint logic programming (CLP). There is also interesting research conducted by [PERSON] et al. [11] with a comprehensive MILP formulation for time-dependent two-echelon capacitated vehicle routing problem that accounts for vehicle type, traveled distance, vehicle speed, load, multiple time zones, and emissions. The paper offers insight on economies of environmentally friendly vehicle routing in 2E-VRP. The result shows the best solution is obtained from the use of the two-echelon distribution system. Research on 2E-VRP continued to grow even from several previous studies, the application of metaheuristic, and applied in several other areas. [PERSON] et al. [12] developed research in 2E-VRP from the previous research with a branch-and-cut-and-price for 2E-VRP. The algorithm tried reformulation based on q-routes that combines two important features. First, the symmetry issues observed in a formulation coming from a previous study of the problem that has been overcome. Second, it is strengthened with several classes of valid inequalities. [PERSON] et al. [13] in their paper addressed two optimization problems. The two-echelon routing problem and two-echelon location problem. The problems will be combined to produce itineraries to deliver goods to customers, with transit through intermediate facilities. The paper purposes hybrid metaheuristic approach which combines enumerative local search with destroy-and-repair principles, as well as some tailored operators to optimize the selections of intermediate facilities. [PERSON] et al. [14] conducted research with multi-echelon distribution strategy to minimize environmental impact as a consequence of logistic operations.