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This paper presents an offline reinforcement learning framework for optimizing SLAM throughput control in warehouse fulfillment environments, balancing throughput maximization with downstream stability. The approach is algorithm-agnostic and demonstrates that the CQL policy improves system health by 22.97% and reduces throttling duration by 3.18%.
This paper proposes a Transformer-based framework for forecasting passenger queue lengths and waiting times at departure gates and security checkpoints in airport terminals, achieving accurate predictions up to two hours ahead to support proactive congestion management.