Dynamic Optimization Strategy for Pod Return Location in Intelligent Warehouses
Intelligent warehouses are extensively utilized in high-frequency picking scenarios,with their efficiency being significantly influenced by pod placement.Fluctuations in orders,caused by seasonal demand changes,marketing strategies,promotional activities,and other factors,drive the dynamic adjustment of pod placement according to demand.Leveraging the feature of intelligent warehouses,a dynamic scheduling model is established during the task cycle with the objective of minimizing completion time of mobile robots.The strategy space,formulated by three pod placement allocation strategies,is constructed.And pod heat,inter pod correlation,existing pod placement are integrated to form a comprehensive state value.A weighted double Q-learning algorithm is designed to derive a dynamic optimization strategy for pod placement allocation,ensuring adaptability to fluctuations in order status.Simulation experiments demonstrate that,the dynamic strategy achieves an efficiency improvement of approximately 31.36％compare to returning to the original placement in the presence of large order fluctuations.The dynamic strategy is capable of promptly adjusting its approach upon order fluctuations,thereby enhancing the picking efficiency of intelligent warehouses.
万方数据
维普
