Int. J. Manufacturing Technology and Management, Vol. 20, Nos. 1/2/3/4, 2010 259 Copyright © 2010 Inderscience Enterprises Ltd. Supervisory-based capacity allocation control for manufacturing systems Karim Tamani* and Reda Boukezzoula Laboratoire d’Informatique, Systèmes, Traitement de l’Information et de la Connaissance – LISTIC, University of Savoie, Domaine Universitaire – BP 80439, 74944 Annecy le Vieux, Cedex France E-mail: karim.tamani@univ-savoie.fr E-mail: reda.boukezzoula@univ-savoie.fr *Corresponding author Georges Habchi Laboratoire Systèmes et Matériaux pour la Mécatronique – SYMME, University of Savoie, Domaine Universitaire – BP 80439, 74944 Annecy le Vieux, Cedex France E-mail: georges.habchi@univ-savoie.fr Abstract: This paper aims at developing supervisory control architecture in order to improve the performance of manufacturing systems. The proposed control architecture is hierarchical. It is composed of basic-level fuzzy logic controllers supervised by a higher level decision-making combining local and global performance indicators to allocate the production capacity. The global performance indicator used in the supervisory level evolves in a tolerance interval defined by the normal operating conditions of the process. When a performance indicator value is outside the predefined tolerance interval, abnormal behaviour occurs. In this case, the supervisor allocates the production capacity or reduces the production throughput according to the aggregated global performance indicators. The simulation results for two applications of manufacturing systems are presented to illustrate the feasibility of the proposed approach. Keywords: manufacturing systems; performance indicators; fuzzy control; supervisory control; aggregation operator; continuous simulation. Reference to this paper should be made as follows: Tamani, K., Boukezzoula, R. and Habchi, G. (2010) ‘Supervisory-based capacity allocation control for manufacturing systems’, Int. J. Manufacturing Technology and Management, Vol. 20, Nos. 1/2/3/4, pp.259–285. Biographical notes: Karim Tamani is a PhD candidate in Automatic and Electrical Engineering at the University of Savoie. The main subject of his thesis includes the development of fuzzy control methodologies based on the fusion of performance indicators in the simulation of manufacturing systems.