Adaptive job-shop scheduling with routing and sequencing flexibility using expert knowledge and coloured Petri nets

Petri nets are known to be efficient for modeling manufacturing systems, because they have a graphical representation and a well-defined semantics allowing format analysis. Considering conflicts as routing and sequencing alternatives, we propose a knowledge based algorithm for online scheduling, that guides the search for a near optimal schedule in the state space efficiently and limits the state space explosion problem. Taking into account that expert knowledge is formulated mostly in natural language, the inference process is modeled by an approximate reasoning scheme consistent with possibility theory. For refining initial knowledge, a concept is presented that combines reinforcement learning techniques with a possibilistic clustering method. Finally, our approach is validated by a numerical example, showing especially that the use of expert knowledge heuristically guides the search for a near optimal solution of the scheduling problem.