Research on Intelligent Logistics Warehouse Scheduling Optimization Based on Integrated Reinforcement Learning

The rapid evolution of intelligent logistics has driven the need for efficient and adaptive warehouse scheduling solutions, particularly in dynamic and uncertain environments. Traditional scheduling methods, including rule-based heuristics and mathematical optimization models, struggle to handle real-time variability and computational complexity, limiting their practical deployment. These conventional approaches often fail to integrate real-time data and adapt to changing supply chain conditions, leading to inefficiencies in routing, resource allocation, and delivery times. To address these challenges, we propose an adaptive logistics optimization framework that integrates reinforcement learning with real-time decision-making. Our model formulates warehouse scheduling as a multi-objective optimization problem, balancing cost, efficiency, and service quality while incorporating real-time traffic conditions, shipment demands, and uncertainty modeling. We leverage a dynamic reinforcement-based strategy that continuously refines decision-making using historical data and real-time feedback. The approach is designed to optimize vehicle routing, order fulfillment, and fleet coordination under stochastic conditions. Experimental evaluations demonstrate that our proposed method outperforms traditional optimization techniques, achieving significant improvements in cost reduction, delivery efficiency, and adaptability to dynamic disruptions. This research contributes to the advancement of intelligent logistics by offering a robust, data-driven scheduling optimization framework suitable for modern supply chain environments.