Solving batch process scheduling/planning tasks using reinforcement learning
The complex and dynamic nature of shop-floor environments, coupled with unpredictable market demands, makes batch plant's reactivity a crucial management issue. In this work, reinforcement learning and a repair-based search strategy are integrated together in a learning problem-solver for scheduling/planning tasks. The overall design of the learning algorithm is based on a state-space search perspective in which the associated optimization problem is solved by starting in some initial infeasible solution and then proceeding to progressively repair intermediate solutions until a feasible one is found. A key component to accelerate search in the state space is learning an evaluation function that accumulates context-dependent knowledge about the goodness of applying a small set of repair operators so that future (re)scheduling and (re)planning problems can be solved with less effort. A demonstrative example is used to illustrate the importance of improving plant reactivity by learning to repair infeasible plans and schedules. (C) 1999 Elsevier Science Ltd.
scheduling learning batch process management combinatorial optimization