A reproducible simulation of an inland railway container terminal under stochastic arrival and finite yard capacity constraints

Global containerization has become a fundamental component of international supply chains, yet container terminals continue to face challenges caused by the stochastic and uneven nature of cargo flows. These issues are particularly pronounced in landlocked countries such as Uzbekistan, where terminals must handle increasing volumes of foreign trade under limited infrastructure capacity. The article presents the developed a discrete-time simulation model based on the Monte Carlo method to quantitatively evaluate the joint effects of container arrival variability, service uncertainty, and batch dispatch rules on inventory dynamics and terminal congestion risk. Each scenario is evaluated using 3000 independent simulation replications with a horizon of 365 days, including a 60-day warm-up period and 305 days of steady-state observation. The terminal operates under next fixed constraints: yard capacity C = 60 containers, dispatch batch size Q = 40 containers per train, and at most one train departure per day. Six configurations of demand and service parameters (λ, μ) are examined. The results reveal a pronounced threshold behavior of the system. The annual number of formed trains increases from 0.01 under low demand to 31.99 under peak load conditions. In the base scenario (λ = 5.2, μ = 4.8), the average steady-state inventory equals 18.73 containers (31.2 % yard utilization), while approximately 4.65 trains are formed per year and no overflow events occur. The probability of congestion remains extremely low even in the most demanding scenarios. The findings show that inventory accumulation is a more sensitive early indicator of operational stress than binary congestion measures, providing important insights for capacity planning and optimal batch-size decisions at inland railway container terminals.

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