This consideration introduces an additional variable, resulting in the Pickup and Delivery Problem with Time Windows and Transshipment (PDPTW-T)  
Although PDP has been addressed for some time ([PERSON] et al., 2018), most problems related to it do not consider uncertainty. Typically, the approaches used are deterministic, meaning that it is assumed that the problem data are known beforehand, which can lead to solutions that are considered admissible, but may not be valid in practice due to data variations. (2018).The integration of agent-based simulation (ABS) and discrete-event simulation (DES) techniques represents a robust and innovative approach to overcoming the limitations of traditional models in urban logistics. As highlighted by [PERSON] et al. (2018), simulation techniques provide a dynamic, adaptable, and detailed framework capable of modeling complex and heterogeneous interactions among the various agents and processes that comprise urban logistics networks. This approach is particularly effective for addressing scenarios involving environmental uncertainties and variabilities, such as traffic fluctuations, delays in pickups and deliveries, and unforeseen changes. Furthermore, studies such as those by [PERSON] (2018) and [PERSON] and [PERSON] (2018) have applied their research to PDP, demonstrating the applicability and effectiveness of ABS integration in capturing emerging behaviors, enabling in-depth analyses of logistics operations and evaluating performance optimization strategies under realistic conditions. These methods are fundamental in solving problems such as the PDP and its variants by combining flexibility in modeling operational dynamics with the capability to test scenarios and make data-driven decisions. This integrated approach stands out as an important tool for improving the efficiency, sustainability, and resilience of operations. For further insights into the application of simulation in logistics, we recommend the work of [PERSON] et al. [11]. This study applies an ABS model to analyze the PDP-TW in a real-world case study in Lisbon. The company specializes in luggage collection and delivery, ensuring adherence to client-specified time constraints and, when necessary, utilizing temporary storage facilities. For comparison, a pre-existing optimization model, based on PDP-TW and adapted to the specific requirements of this case, was incorporated to represent the steady-state behavior of operations. The ABS model captures the dynamic interactions between key logistical entities, such as transport vehicles, pickup points, delivery locations, and storage facilities, offering a comprehensive representation of the company's logistics network. By integrating simulation and optimization, the study aims to compare the company's current operations with an optimized scenario, evaluating route efficiency, cost minimization, and time window adherence. Furthermore, the research explores the impact of route optimization on sustainability, highlighting the reduction in total traveled distance and its direct correlation to CO2 emissions, given that the company operates a non-electric vehicle fleet. The findings demonstrate that optimization and simulation together can significantly enhance operational efficiency, providing actionable insights for improving resource allocation and urban logistics performance. This paper is structured as follows. In Section 2, a focused literature review covering existing research on methodologies used in urban logistics, including ABS for the context of PDPs, is presented. In Section 3, the material and methods are detailed, describing the problem context, the PDP-TW mathematical model, and the ABS model. Section 4 describes the case study in the city of Lisbon, including the validation of the proposed simulation model (ABS). Section 5 presents the results and discussion of the simulation with data provided by the company under study. Finally, conclusions, implications and possible future research directions are presented in Section 6.