The present application relates to a kind of based on
artificial intelligence warehouse
inventory management and optimization method, by comprehensively considering commodity storage location, tray partition, order priority and
system state etc., construct new
mathematical model, and use deep
reinforcement learning DQN network solution, realize
intelligent decision commodity pick-up location and generate optimal picking path;The method provided by the present application can effectively shorten
order processing time, for the demand of small batch, multi-category order, improve warehouse efficiency, reduce
stacker moving distance and pick-up frequency, reduce
operating cost, and can be flexibly scheduled according to priority, adapt to
system real-time state change;Compared with traditional method, after adopting the optimization strategy provided by the present application, the number of tray extraction is significantly reduced, has significant beneficial effect, can be widely applied in intelligent warehousing field, improve the
automation,
informatization and intelligent level of warehousing management, meet the demand of modern logistics industry to efficient warehousing
system.