## 1 Introduction  
In recent decades, digitalization has become a key driver in optimizing logistics processes, transforming port management worldwide. As maritime terminal operations become more complex and the need for greater efficiency and sustainability in freight transport grows, advanced technologies within the Industry 4.0 framework have gained increasing importance [1]. Managing roll-on/roll-off (Ro-Ro) terminals, where vehicles must be efficiently organized and loaded, presents several challenges, including space allocation, internal route optimization, and the coordination of loading flows. In this context, digitalization provides innovative solutions that enhance decision-making and improve operational efficiency through process automation [2]. Maritime terminal operations, particularly in roll-on/roll-off (Ro-Ro) terminals, present complex logistical challenges where optimizing vehicle flow and space allocation is critical for operational efficiency. One of the most promising advancements in port digitalization is the use of multi-agent systems (MAS), an artificial intelligence (AI)-based technology that enables multiple autonomous entities to interact and coordinate efficiently [3]. These systems have proven highly effective in logistics and transportation, as they allow for decentralized decision-making, reduce reliance on human operators, and adapt to unexpected operational changes. In port environments, intelligent agents can improve vehicle distribution, reduce waiting times, and optimize internal traffic, ultimately streamlining cargo handling operations [4]. Beyond operational improvements, adopting intelligent agents in ports aligns with sustainability efforts and environmental goals [5]. Optimized traffic flow and route planning can help reduce energy consumption and lower CO2 emissions [6]. Additionally, automating the planning and distribution of vehicles minimizes idle times and improves port infrastructure utilization, leading to reduced operational costs and greater competitiveness for Ro-Ro terminals in an increasingly demanding market [7]. Despite significant progress in digitalization, many terminals still rely on traditional management systems, which often struggle with flexibility, responsiveness, and efficiency [8]. Current space allocation and route planning models are typically based on fixed rules, making it difficult to adjust to fluctuating demand, unexpected congestion, or delays in vessel arrivals. In contrast, multi-agent systems can continuously analyze terminal conditions and adapt operations in real time, enhancing efficiency and resilience [8]. This paper aims to propose a conceptual and architectural model for multi-agent system (MAS) deployment in Ro-Ro terminal management. Given the complexity and variability of real port environments, establishing a theoretical foundation is a necessary preliminary step before undertaking pilot implementations or simulation validations. This study aims to develop and evaluate a multi-agent model to optimize Ro-Ro terminal operations, focusing on key aspects such as space allocation, route planning, traffic management, and synchronized loading. Unlike conventional approaches, the proposed system leverages autonomous agents to improve coordination and adapt to changing port conditions. To assess its effectiveness, a comparative analysis will be conducted, measuring the system's performance against traditional management strategies in terms of reducing operational times and optimizing resource use. This research defines a conceptual framework based on digitalization and artificial intelligence to systematically improve vehicle flow, reduce operational delays, and increase efficiency in Ro-Ro terminal logistics. The integration of intelligent agents introduces a scalable approach to logistics management, enhancing automation and responsiveness in dynamic port environments.