METHOD FOR USING REINFORCEMENT LEARNING TO OPTIMIZE ORDER FULFILLMENT

An order fulfillment control system for a warehouse in accordance with the present invention includes a controller, a memory module, and a training module. The controller controls mobile autonomous devices, fixed autonomous devices, and issues picking orders to pickers. The controller controls fulfillment activities in the warehouse using hierarchically tiered algorithms, and records operational data for the fulfillment activities in the warehouse. The memory module holds the operational data. The training module retrains the algorithm using reinforcement learning. The training module performs the reinforcement learning on the operational data to retrain/update the algorithms. The training module retrains a macro algorithm according to a first set of priorities for optimal operation of the warehouse and retrains a plurality of micro algorithms according to corresponding second sets of priorities for optimal operation of a particular location and/or activity within the warehouse. The controller adaptively controls the fulfillment activities using the updated algorithms.