article · Logistics Supply Chain Sustainability and Global Challenges
Container terminal operations rely on effective yard storage strategies to minimise unnecessary shifting of cargo. Research focusing on Lekki Freeport Terminal, an automated maritime hub in Nigeria, examines how container dwell time influences throughput and rehandling productivity. By combining a deterministic slot-based rehandling structure with Monte Carlo simulations, the study models the vertical inefficiencies of dense stacking alongside retrieval uncertainties. The analysis identifies a non-linear link between stack height and rehandling, where rehandles rise from zero at a single tier to 1.22 at 2.29 tiers. Crucially, incorporating realistic, heterogeneous retrieval patterns drives expected rehandles above 1.9 even at low stack heights. These results show that assuming uniform retrieval probability underestimates terminal congestion and handling costs, underscoring the necessity of stochastic, data-driven yard planning.
Maritime container terminals must balance dense storage against the delays caused by constantly rearranging containers. By demonstrating how uneven container retrieval patterns significantly worsen handling bottlenecks, this work provides a clearer basis for managing automated yards. It helps terminal managers avoid unexpected delays and operational expenses by adopting more realistic, data-driven planning models.
The findings can inform the design of yard management algorithms and terminal operating systems for automated maritime facilities such as Lekki Freeport Terminal. Prospective users include port authorities, terminal operators, and logistics software developers aiming to optimise stacking rules and cost projections. The work is at an applied simulation stage, providing quantitative models that could be integrated into operational planning software, although direct software implementation is not detailed in the abstract.
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Abstract This research investigates how container dwell time affects the throughput and rehandling productivity at maritime terminals with focus on Lekki Freeport Terminal (LFT) - an emerging automated maritime hub in Nigeria. A deterministic slot-based rehandling structure is first established to characterize the vertical inefficiency embedded in dense stacking configurations, after which Monte Carlo Simulation is applied to capture uncertainty in container retrieval. Results show strong statistical consistency of the simulation, with the expected number of rehandles converging tightly to a theoretical mean of 2.00 as sample size increases. Scenario analysis reveals a nonlinear relationship between average stack height and rehandling intensity: expected rehandles rise from 0 at a stack height of 1.00 to 1.22 at 2.29 tiers. When heterogeneous retrieval behavior is introduced, expected rehandles increase substantially, exceeding 1.9 at low stack heights and remaining significantly higher than under equal-probability assumptions. These findings demonstrate that ignoring demand heterogeneity leads to systematic underestimation of congestion and handling costs. The study provides quantitative evidence to support stochastic, data-driven yard planning as a basis for improving operational efficiency and economic sustainability in emerging container terminals.
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DOI: 10.2478/jlst-2026-0007
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