Amsterdam Airport Schiphol (AAS) faces operational challenges from fluctuating baggage volumes and limited infrastructure, necessitating innovative approaches to optimize baggage handling. This study focuses on strategic buffering of super cold transfer baggage to reduce peak occupancy at transfer infeed points. A simulation model, integrated with TBATS forecasting, evaluates three buffering strategies (fixed target, polynomial, hybrid) and two reintroduction strategies (early, optimized release) under normal, disrupted, and highdemand scenarios. The results demonstrate peak occupancy reductions of up to 26.8%, with the polynomial buffering strategy and optimized release proving the most effective, though requiring additional AGV resources. Fixed target strategies offer a more resource-efficient alternative while maintaining substantial improvements in peak shaving. Early release strategies further reduce buffer congestion, optimizing operations for space-constrained environments. This research provides actionable insights for AAS and similar airports, highlighting trade-offs between efficiency, resource requirements, and operational constraints. The findings contribute to scalable solutions for managing growing baggage volumes within existing infrastructure, enhancing system resilience and performance.