###### Abstract  
Dar es Salaam Port, a crucial logistical hub in East Africa, faces significant challenges related to cargo handling efficiency, road congestion, and capacity constraints. The port's performance is pivotal for regional trade, necessitating a comprehensive analysis to identify and address operational inefficiencies. This study employed Markov processes to evaluate cargo handling and delivery times, cellular automata for simulating road traffic dynamics, and max-flow models to optimize cargo flow from the port to hinterland destinations. The analysis incorporated factors such as road and rail capacities, traffic conditions, and environmental impacts. The Markov process model indicated that cargo spends 15% of its time waiting at the port, 50% in transit, and 10% delayed, with only 25% successfully delivered. The Cellular Automata simulation revealed severe congestion for heavy trucks due to poor road conditions, with an additional 10 min delay during the rainy season. The max-flow model highlighted that while the road and rail networks generally meet demand, significant bottlenecks exist, particularly for Lubumbashi, which faces a capacity shortfall of 500 t/day. The findings offer actionable insights for stakeholders. Logistics operators can leverage the framework to predict delays, optimize resource allocation, and improve delivery reliability. Policymakers can prioritize strategic investments in infrastructure upgrades, traffic management, and road maintenance to reduce delays and congestion. Scholars can adopt the integrated methodology to analyze similar systems. Together, these efforts can enhance Dar es Salaam Port's operational efficiency, reduce transit times, and support regional trade development..  
D 27 January 202510 February 202510 February 202  
result in delays, increased costs, and reduced throughput, all of which hinder the port's competitiveness and efficiency. In response to these challenges, this paper proposes a novel optimization framework that integrates Markov processes, max-flow problems, and traffic flow models to enhance hinterland connectivity. Each of these models addresses specific aspects of the transportation problem. Markov processes are used to model the stochastic events and uncertainties inherent in cargo movement, such as random delays and equipment breakdowns [4; 5]. Max-flow models optimize the throughput of goods from the port to inland destinations, ensuring the maximum use of available infrastructure while respecting capacity constraints [6; 7]. Finally, traffic flow models simulate vehicle movement, allowing for an analysis of congestion and travel times on key routes. The proposed framework will be applied in a case study of Dar es Salaam Port, with the goal of addressing the unique logistical challenges posed by its infrastructure and hinterland network. By integrating these models into a cohesive optimization framework, the study aims to improve the efficiency of goods movement, reduce congestion, and enhance the overall connectivity of the port. The motivation of this study is to address the logistical challenges faced by Dar es Salaam Port, including congestion, infrastructure limitations, and delays that hinder efficient hinterland connectivity. Existing models often fail to capture the complexity of these issues. This study proposes a novel optimization framework combining Markov processes, max-flow problems, and traffic flow models to improve cargo flow, reduce delays, and enhance the port's operational efficiency. The goal is to provide a comprehensive solution for optimizing hinterland connectivity, with real-world applications for ports globally. While previous studies have focused on optimizing specific aspects of hinterland connectivity, such as transportation routes or infrastructure capacity, they often fall short of addressing the combined and dynamic challenges faced by ports. These approaches typically neglect the stochastic nature of logistics, including random delays and unpredictable events that affect cargo flow.