Self-organized holonic manufacturing systems combining adaptation and performance optimization

Abstract

Traditional manufacturing solutions, based on centralized structures, are ineffective in unpredictable and volatile scenarios. Recent manufacturing paradigms, such as Holonic Manufacturing Systems, handle better these unpredictable situations but aren't able to achieve the performance optimization levels displayed by the classical centralized solutions when the system runs without perturbations. This paper introduces a holonic manufacturing architecture that considers biological insights, namely emergence and self-organization, to achieve adaptation and responsiveness without degrading the performance optimization. For this purpose, self-organization and self-learning mechanisms embedded at micro and macro levels play an important role, as well the design of stabilizers to control the system nervousness in such dynamic and adaptive behaviour. © 2012 IFIP International Federation for Information Processing.