Abstract: 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.

Abstract: One or more simulations are generated for in-home monitoring. The simulations model sensory detection of a user's physical activities using a different number and/or a different combination of sensors. Each different simulation may thus be associated with an accuracy and a cost, depending on the number and/or combination of sensors. The simulations thus present a range of sensory configurations that balance accuracy and affordability, from which an optimum sensory solution may be determined for the in-home monitoring.