Abstract
This paper deals with a self-organizing manufacturing system (SOMS) which is introduced the concept of cellular robotic system. This system consists of two major layers. The lower one is a self-organization module, which has self-organizing ability for fitting other processes and environments into the intelligent manufacturing system. The higher one is a decision-making module, which is activated by interaction among processes. The manufacturing environment includes many optimization problems which are defined as ill-defined structures. There are stochastic search methods for these problems such as the simulated annealing and the genetic algorithm (GA). In this study, a method for optimizing the manufacturing system using the age-structured GA (ASGA) as the self-organization ability is proposed. The ASGA is applied to a press machining line which is capable of reorganizing the machine in itself, as an example of the SOMS. The effectiveness of the proposed method is demonstrated through numerical simulations.
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