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

Ey, H.; Sackmann, D.; Mutz, M.

In: Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC'2000), 8-11 October 2000, Nashville, TN, pages 3212-3217. 2000.

Abstract: Petri nets are known to be efficient for modeling manufacturing systems, because they have a graphical representation and a well-defined semantics allowing formal analysis. Considering conflicts as routing and sequencing alternatives, the paper proposes a knowledge based algorithm for on-line 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 probability theory. For refining initial knowledge, a concept is presented that combines reinforcement learning techniques with probabilistic clustering methods. Finally, the proposed 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.

Keywords: approximate reasoning, colored Petri nets, possibilistic clustering, reachability analysis, reinforcement learning, rule induction, scheduling.

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