## 2 Literature Review  
Urban logistics is an area of extreme importance for the functioning of a city, since it is directly related to the transportation of goods, services and people. However, urban logistics presents several challenges, such as the complexity of transportation systems, the variation in demands, and the presence of multiple agents involved [10]. In urban logistics, the supply chain must be highly efficient and adaptable to meet growing demands, especially those driven by the advancement of e-commerce and the need for fast deliveries in densely populated areas. As highlighted by [PERSON] et al. [12], the strategic location of logistics centers plays a fundamental role in the performance of the supply chain, contributing to the reduction in operational costs and the improvement of efficiency in the distribution of goods. In addition, the integration of these centers with optimized transportation routes ensures the agile and economical delivery of products, a critical aspect in urban environments marked by intense competition for space and time resources. The strategic location of logistics centers and their integration with optimized routes, which are fundamental for the efficiency of urban logistics, have direct implications for the solution of Vehicle Routing Problems (VRPs), especially in the subcategory known as PDP. PDP involves the transportation of objects or people between specific origins and destinations and can be classified into three main categories. The first group, many-to-many, allows any node to function as an origin or destination for any commodity. A classic example is the exchange problem, as described by [PERSON] and [PERSON] [13], where each node initially contains one type of commodity and requires another specific type. The second group, one-to-many-to-one, addresses scenarios in which goods are sent from a warehouse to several customers, with pickups returning to the warehouse, as in the study by [PERSON] and [PERSON] [5], which proposes optimized solutions to maximize operational efficiency. Finally, the one-to-one group encompasses situations in which goods have specific origins and destinations, common in express mail operations and door-to-door services. [PERSON] et al. [14] explored this category by analyzing differences between taxi and ridesharing, highlighting that both approaches involve transportation requests with a defined origin and destination. The growth of e-commerce, combined with advances in information technology, which allows online service requests as observed in the company under study, has generated the need for new approaches to optimize transportation systems. One such approach is the inclusion of transshipments in the logistics process, resulting in the PDP-T. Recent studies, such as [PERSON] et al. [15], explore the PDP-T by analyzing how uncertainties in goods handling can be managed to improve logistics performance, highlighting its potential in complex and high-demand urban scenarios. For a detailed overview of the main works on PDPs applied to urban logistics, see [PERSON] et al. [2], which is a systematic review of three important approaches in logistics: PDP, TLP and LRP. However, recent methodological developments in PDP-TW literature demonstrate a shift towards more adaptive, realistic, and robust models that incorporate uncertainty and operational constraints. For example, [PERSON] [16] propose a robust optimization framework that integrates travel time uncertainty directly into the mathematical formulation of PDP-TW, generating solutions that maintain feasibility across a predefined set of adverse scenarios improving resilience in fluctuating traffic conditions. Similarly, [PERSON] et al. [17] introduce the PDPTW-DB (Driver Breaks) model, a Mixed-Integer Linear Programming (MILP) based formulation that incorporates driver break regulations, illustrating the importance of regulatory compliance and human constraints within route planning, elements commonly overlooked in traditional PDP-TW formulations.