SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning

Abstract

With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update through learning of rescheduling knowledge using simulated transitions of abstract schedule states is proposed. Deictic representations of schedules based on focal points are used to define a repair policy which generates a goal-directed sequence of repair operators to face unplanned events and operational disturbances. An industrial example where rescheduling is needed due to the arrival of a new/rush order, or whenever raw material delay/shortage or machine breakdown events occur are discussed using the SmartGantt prototype for interactive rescheduling in real-time. SmartGantt demonstrates that due date compliance of orders-in-progress, negotiating delivery conditions of new orders and ensuring distributed production control can be dramatically improved by means of relational reinforcement learning and a deictic representation of rescheduling tasks.