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Modeling and optimization of kanban controlled manufacturing systems with GSPN including QN.

Becker, M.; Szczerbicka, H.

In: Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC'98), 11-14 October 1998, San Diego, CA, pages 570-575. 1998.

Abstract: This paper investigates the kanban assignment problem for assembly kanban systems. REMO, a general purpose tool for optimization is used. REMO includes several algorithms like hill climbing and genetic algorithms and has an easily adaptable interface to performance analysis tools. For finding an optimal kanban assignment with respect to certain performance measures of the system, a fast performance analysis is a crucial factor for sensible and successful application of optimization algorithms. Petri nets including queueing nets (PNiQ) are introduced for this purpose, as a modeling formalism. At modeling level, PNiQs allow the use of both the concise description of queueing nets where possible and the notation of stochastic Petri nets where needed, e.g., to model fork. /join needed for the matching of kanbans and parts. Approximate performance analysis is carried out by decomposition and aggregation of the queueing net parts. This technique provides a fast numerical solution even for large systems as important requirement for the application of optimization algorithms. The optimization not only yields optimal kanban assignments for various kanban systems but also a common pattern in the set of solutions can be recognized.

Keywords: PNiQ, REMO, kanban systems, manufacturing systems, queueing systems, stochastic Petri nets.

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