# Urban Logistics through River: A Two-Echelon Distribution Model
## 4 Results  
The solution procedure was coded in Python, and experiments were run on a computer with processor Intel(r) Core(tm) i7-10510U, CPU at 2.3 GHz, and 16 GB RAM. Two main scenarios were evaluated. Regarding the scenarios, Scenario 1 considers three fluvial satellites located in predetermined ports next to the Seine River; these satellites do not have limited capacity. In addition, a barge is the transportation mode used to serve the satellites in the first echelon and e-cargo bikes in the second echelon. Scenario 2 consists of a set of three uncapacitated satellites located inside Paris and are not fluvial ports. The distribution network in this scenario has a heterogeneous fleet. The fleet of vehicles is composed of delivery vans (diesel-fueled) for the first echelon, and the  
Figure 2: Distribution of customers, fluvial satellites, and road satellites in Paris. second echelon has the same transportation mode as Scenario 1. For the generated instance, the transportation modes considered will have load capacities that are proportional to the type of vehicle. These are not the actual capacities for the purpose of this research. Further studies will be done to extend this problem to the actual instance, which comprises more than 90,000 delivery points. The dataset is provided by a French parcel delivery company whose name must be kept confidential. The capacity of the barge, delivery van, and e-cargo bike is respectively 50 kg, 45 kg, and 8 kg. Key performance indicators were considered to compare both scenarios and see how sustainable they are. The number of vehicles, CO\({}_{2}\)e emissions, fixed cost, energy consumption, and land use were some of them. The CO\({}_{2}\)e is a universal measurement used to indicate, in terms of CO\({}_{2}\)e, the equivalent of each of the greenhouse gasses with respect to its potential for global warming [48]. Land use is considered primarily to ensure that the distribution network does not invade public space in large proportions. Fixed costs allow decision-makers to determine which scenario suits their needs better. Energy consumption allows for monetization of the cost of the energy (diesel or electricity) that each type of vehicle needs to perform the delivery activities. Table 1 shows the factors that were used to calculate the indicators and the average travel speed for each type of vehicle. For first-echelon results, see Table 2. Scenario 1 has the best results in terms of distance, CO\({}_{2}\)e emissions, fixed cost, and energy consumption, as these indicators depend on the distance traveled by the barge. However, due to the travel speed of this transportation mode and the maximum speed allowed on the Seine River, which is 15 km/h, the travel time is longer than Scenario 2. Moreover, the area occupied by the barge in the river is 66.98 m\({}^{2}\) since the barge is bigger than the delivery van selected in this study. In Scenario 2, the complete delivery operation of the first echelon can be performed in 2.89 h, which is 1.12 h faster than in Scenario 1, even if the distance is greater to arrive at the desired delivery point (satellite). In the second echelon, the customers are clustered in three groups that are the number of satellites in each scenario. This clustering was performed to reduce the distance between the delivery points and the satellite (centroid of the cluster) in such a way that clients are allocated to the closest satellite. Table 3 shows the results for both scenarios per cluster and the total values for each indicator per scenario. For both scenarios, the last-mile delivery is performed by an e-cargo bike. This means of transportation has a limited route duration of 1.5 h per tour due to the duration of the battery. Scenario 2 requires more e-cargo bikes