The aim of this paper is to form the best collaborative networked organization of small manufacturers to fulfill a production plan. Manufacturing networks can be formed using two approaches. The conventional approach is the formation of networks by the grouping of the best manufacturers based on multiple criteria. The proposed approach is the formation of all feasible networks followed by the grouping of the best networks based on multiple criteria. The hypothesis of this paper is that the better group of manufacturing networks may not be the result of the selection of the best available manufacturers to form each network, based on the same criteria. The research methodology employed involves techniques such as interaction protocol development, ontology engineering, and principles from concepts such as the contract net protocol, the product-resource-order-staff-architecture, the auctioning process, agent-based modeling, and rule-based programming. Results from the proposed approach were compared to the conventional approach on multiple criteria namely lead time, quality, cost, and delivery reliability. On the basis of the results, the hypothesis was not rejected.Index Terms-Contract net protocol (CNP), holon, network, ontology, protocol, schedule.

This paper aims to create a predictive model, which will assist in the allocation of newly received orders in a manufacturing network. The manufacturing network, which is taken as a case study in this research, consists of more than 300 small manufacturing enterprises with a central company as the project managing integrator. The methodology presents the mapping of a PROSA (Product-Resource-Order-Staff Architecture) based ontology model on a decision tree, which was created with the Waikato Environment for Knowledge Analysis (WEKA) application. Furthermore, the methodology also demonstrates the formulation of the Semantic Web Rule Language (SWRL) rules from the WEKA decision tree with the help of MATLAB programming. The paper validated the result generated by the ontology model with the results of the decision tree model.

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This paper investigates the development of an intelligent data query framework through the use of semantic web technologies for manufacturing purposes. The primary objectives of the ontologybased data query were to develop an efficient and scalable data interoperability and retrieval system; in order to find the most relevant query results with minimum message cost, most hits per query and least response time. This document explains the idea of ontology and the application of the same in the manufacturing domain. A computer simulation software was developed based on a real case study of distributed networks of manufacturing workshops. In this research, a semantic query algorithm was developed where query results are returned by investigating the semantic richness of each workshop. Results were compared with a semantic-free search mechanism based on key performance indicators. The results show the validity of the proposed model for efficient data query when compared to random search.

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