Data-Driven Adaptive Dispatching Policies for Processing Networks

This letter presents and analyzes an adaptive data-driven controller that learns the optimal processing rate in a multi-unit processing network in the presence of disturbances. We formulate an optimization problem of linear cost, linear dynamics for the processing network model and an affine constraint on the dispatcher policy. A data-driven linear equation is constructed, based on which the online dispatcher policy is updated. An upper bound on the gap between the optimal cost and the cost incurred by the data-driven controller is extracted.