## 2 Literature Review
The paper considered short-term tactical problem named two-echelon time-constrained vehicle routing problem (2E-TVRP) which was a little bit different from 2E-VRP. The result shows the scheduling provided by 2E-TVRP is promising to reduce the CO\({}_{2}\) emissions per ton-kilometer of the linehaul-delivery system by adjusting the central depot location or developing the loaded-semitrailer demand among O-D pairs to eliminate empty-running of tractors. [PERSON] et al. (2015) applied 2E-VRP in transporting goods in urban logistics to reduce traffic congestion that might occur. [PERSON] et al. (2016) in their research applied a metaheuristic approach based on variable neighborhood search (VNS) and integer programming to solve 2E-CVRP-E. The integer programing has a role to find better optimal result after VNS algorithm was applied. To validate its effectiveness, the metaheuristic first performs tests on 2E-CVRP instances and improves 13 current best-known solutions out of 234 instances. [PERSON] et al. (2017) proposed a genetic-algorithm-based (GA) approach to solve the 2E-CVRPSD. A simple encoding and decoding scheme, a modified route copy crossover operator, and a satellite-selection-based mutation operator are devised in this approach. The numerical results show that for all instances, the expected cost of the best 2E-CVRPSD solution found by the proposed approach is not greater than that of the best-known 2E-CVRP solution with an average relative gap of 2.57%. Therefore, the GA-based approach can find high-quality solutions for the 2E-CVRPSD. [PERSON] et al. (2018) formulated the problem as a mixed 0-1 linear program and proposed five families of valid inequalities to strengthen the model. Based on the model and the inequalities, we implement a branch-and-cut algorithm to solve the problem. The proposed branch-and-cut algorithm was evaluated on two classes of randomly generated instances. The computational results show that the five families of valid inequalities can substantially improve the lower bounds yielded by the LP relaxation of the model, and the branch-and-cut algorithm can solve more instances to optimality than CPLEX. We also conduct additional experiments to analyze the impacts of the grouping constraints on the problem and illustrate the differences between the 2E-CVRPGC and the 2E-CVRP. [PERSON] and [PERSON] (2019) tried to minimize the total travel cost, customer waiting time, and carbon dioxide emission in 2E-CVRP with perishable food. The proposed model is a mixed-integer non-linear programming (MINLP). By applying some linearization methods, the MINLP model exchanged to a mixed-integer linear programming (MILP). This paper uses a non-dominated sorting genetic (NSGA-II) algorithm to solve the presented mathematical model. The related results would be compared with Lp-metric results in small-sized test problems and with multi-objective particle swarm optimization (MOPSO) algorithm in medium and large-sized test problems. In order to evaluate the quality of the solution sets, the results of two meta-heuristic algorithms are compared based on four comparison metrics in medium-sized problems. The obtained results indicate the efficiency of the NSGA-II algorithm. [PERSON] et al. (2019) introduced a new routing model that considers a synchronized truck-drone operation by allowing multiple drones to fly from a truck, serve one or multiple customers, and return to the same truck for a battery swap and package retrieval. The model addresses two levels (echelons) of delivery: primary truck routing from the main depot to serve assigned customers and secondary drone routing from the truck, which behaves like a moveable intermediate depot to serve other sets of customers. The model takes into account both trucks' and drones' capacities with the objective of finding optimal routes of both trucks and drones that minimizes the total arrival time of both trucks and drones at the depot after completing the deliveries.