In order to remain in contention in the world market, it is necessary for manufacturing enterprises to design and produce new products with the shortest possible lead time as well as in an effective and competitive way. This includes features of a distributed control paradigm, to adjust to dynamic situations such as frequent process disturbances. Feasibility of a manufacturing enterprise can be enhanced by forming an efficient, flexible, and responsive network of autonomous agents, which can be termed an ‘agent networks’. The flexibility and responsiveness of an agent network can be implemented only when there is proper interaction and communication facility between its various entities. The goal of this paper is to streamline the structural intricacies related to the agent network so that the system as a whole can be more flexible and responsive. In this context, a fuzzy c-means-based clustering algorithm is proposed to group the agents in order to capture effectively the uncertainty and imprecision associated with them. This leads to clustering of the pertinent agents and thus reduces the number of discrete entities of the manufacturing system, thereby simplifying the function of the system. A test problem has been solved to show the effectiveness of the proposed approach.
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