This capstone project investigates inefficiencies in truck yard operations at logistics hubs, focusing on a leading U.S. beverage manufacturer's facilities, where prolonged queueing times contribute to increased fuel consumption, elevated emissions, and higher operational costs. The study employs agent-based simulation, implemented through AnyLogic, to model and simulate truck flow within the yard, addressing challenges such as congestion, suboptimal layouts, and the complexity of diverse shipping methods, including less-than-truckload freight, parcel deliveries, and customer pick-ups. The primary objectives are to develop a baseline simulation model for a specific plant — replicating the layout of one of the sponsor company's most critical facilities — to test operational scenarios such as adding a secondary check-in gate, and to deliver a dashboard for what-if analysis to evaluate potential improvements. By integrating real-world data on truck arrival patterns, yard configurations, process times, and operational rules, the simulation captures the dynamic interactions of autonomous entities within the system, offering a granular analysis of spatial competition, temporal coordination, and behavioral adaptations. The project identifies high-quality yard configurations and strategies that minimize queueing times, enhance throughput, and improve overall efficiency under diverse circumstances, providing the sponsor with a data-driven tool to support decision-making for existing and new facilities. The project also offers actionable recommendations and scenarios, contributing to more sustainable and cost-effective logistics operations by reducing turnaround times and operational bottlenecks.
