# Urban Logistics through River: A Two-Echelon Distribution Model
## 1 Introduction  
In the last five years, an expansion of e-commerce has been taking place worldwide, reaching a growth of around 21% [1]. The strong measures that several countries implemented to deal with the COVID-19 pandemic, such as the lockdown and home office, not only reinforced the growing trend in 2020 [2] but also caused supply chain and logistics managers to rethink their operations [3]. E-shopping offers a lot of possibilities to the consumer in a comfortable way, receiving all deliveries in different ways; for example, the two most common last-mile delivery modes are (1) delivery to a selected customer destination point like home delivery or at the workplace. (2) Click-and-collect in a store, mailbox, or at a partner location. Last-mile delivery operations include parcel sorting, transportation, and hand-over to the consumer at the preferred delivery mode and location [4; 5]. In addition, the concentration of population in urban areas is increasing; about 3.5 billion people live in cities [6], and the number will still grow since cities are where the main economic, cultural, entertainment, and educational activities of a country are concentrated. Moreover, according to [7], many home deliveries of online retail orders are small parcels that must be delivered in dispersed points inside urban areas. Indeed, urban freight transportation has become an important component of urban planning, along with many challenges that arise in the operations associated with freight transportation. For instance, last-mile deliveries cause up to 28% of the total delivery costs [8] and are related to negative environmental impact issues such as air pollution and traffic congestion. Transport systems obstruct themselves and other road users by the invasion of public space during pickup and delivery operations in urban areas. European urban freight transportation causes approximately 23% of carbon dioxide equivalent (CO\({}_{2}\)e) emissions [9] and between 30% to 50% of other pollutants in cities [10]. Different city logistics initiatives and strategies have been developed and modeled to improve efficiency, relieve traffic congestion, and reduce GreenHouse Gas (GHG) emissions. One of those strategies is the redesign of urban distribution networks by addingintermediate nodes such as hubs, satellites, or other types of urban logistic spaces [11; 12; 13]. Other strategies are the investment in new logistic resources, information technology systems, and automation technology [14]. When designing the last-mile supply network, the most employed model is the two-echelon distribution system, which consists of delivering parcels from a depot to a set of satellites and from there to a set of customers geographically dispersed. Practitioners and academicians frequently approach this logistics distribution problem as a two-echelon vehicle routing problem (2E-VRP) [15]. Due to the high computational complexity of the problem, many researchers are motivated to design and propose approximate algorithms (heuristics and metaheuristics) to solve it. Multimodal transportation, such as public transport, Unmanned Aerial Vehicles (UAVs), Autonomous Vehicles (AVs), Electric Modular Vehicles (EMVs), Electric Vehicles (EVs), and cargo bikes, to improve the efficiency and sustainability of urban parcel distribution systems. In the context of innovative solutions to reach more sustainable deliveries, our study proposed two scenarios in which one of these aims to use the inland waterways in such a way that water transport can be used in the first echelon as an innovative way for parcel distribution. In addition, the second echelon has a set of electric cargo bikes (e-cargo bikes) that pick up parcels at the ports designated as fluvial satellites, where a barge will deliver the packages for further last-mile distribution. This research aims to fill the gap in addressing these attributes of urban parcel delivery systems. The research to be challenged was presented at a conference [16].