Scheduling truck arrivals for efficient container flow management in port logistics.

Baldouski, D; Krész, M; Dávid, B · 2025 · PubMed Central

DOI: 10.1007/s10100-025-00976-x

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Summary

This paper addresses the optimization of truck arrival scheduling and routing within container terminals to mitigate congestion and improve overall port efficiency. The authors identify that logistical bottlenecks at gates and internal roadways significantly impact operational costs and environmental factors. To solve this, they propose a Mixed-Integer Linear Programming (MILP) model that integrates the scheduling of ships, trucks, gates, docks, and internal road networks. The model aims to minimize total operational costs, defined by the cumulative time trucks spend within the port system, while adhering to strict constraints including First-In-First-Out (FIFO) rules on all road segments, facility capacity limits, processing times, and precedence relationships. The methodology involves a comprehensive mathematical formulation that accounts for a port infrastructure comprising an external parking lot, multiple gates, docks, and connecting roads. The model utilizes binary assignment variables to allocate trucks to specific gates and continuous time variables to track arrival and departure times at each facility. It employs "Big M" constraints and auxiliary binary variables to linearize complex precedence and capacity constraints, ensuring that gates and docks process only one truck at a time and that road capacities are not exceeded. To address computational intractability for large-scale instances, the authors developed a rolling horizon heuristic. This approach divides the set of trucks into sequential, overlapping time horizons based on planned arrival times, solving the problem iteratively while using solutions from previous horizons to inform subsequent ones. The study evaluates the proposed methods using simulated instances based on real-world data from the Port of Koper. The experiments demonstrate the model's ability to handle complex port layouts and traffic patterns. The refined MILP model, enhanced with precedence and ordering constraints, is shown to be more streamlined and capable of solving larger instances than previous iterations. The rolling horizon heuristic effectively manages the complexity of large datasets, allowing for scalable solutions where exact methods would fail. The results indicate that the integrated approach successfully optimizes container flow by coordinating truck movements with ship schedules and dock assignments. The significance of this work lies in its provision of an exact mathematical model that comprehensively integrates the entire terminal network, a gap the authors note in existing literature. Unlike traditional Truck Appointment Systems that focus primarily on gate congestion via discrete time windows, this model seeks an overall optimal solution using a continuous time approach. The findings suggest that such integrated scheduling can significantly reduce delays and operational costs. The authors conclude that the model is versatile and adaptable to various port configurations, offering a robust tool for improving port logistics efficiency and potentially extending to multi-objective analyses involving emissions and energy consumption.

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