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Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning. [ABSTRACT FROM PUBLISHER]

The use of ontologies for knowledge sharing and distributed collaboration has been widely recognised in the knowledge modelling community, but the lack of a systematic and constructive methodology for developing manufacturing ontologies has impeded their wide usage for knowledge reuse in distributed manufacturing environments. This paper presents a constructive, two-level knowledge modelling approach to systematically develop manufacturing ontologies using both software engineering and Semantic Web paradigms. The UML/OCL (Unified Modeling Language/Object Constraint Language)-based object modelling is used first to serve as a graphical and structured basis for conceptual communication between domain experts and knowledge engineers. The OWL/SWRL (Web Ontology Language/Semantic Web Rule Language)-based ontology modelling then extends the UML/OCL-based object models with added semantics using a progressive, semantics-oriented knowledge acquisition method. An illustrative example for manufacturing ontology development in the manufacturing industry for producing electronic connectors is used to demonstrate the practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

As supply chains are becoming ever more global and agile in the modern manufacturing era, enterprises are increasingly dependent upon the efficient and effective discovery of shared manufacturing resources provided by their partners, wherever they are. Enterprises are thus faced with increasing challenges caused by the technical difficulties and ontological issues in manufacturing interoperability and integration over heterogeneous computing platforms. This paper presents a prototype intelligent system SWMRD (Semantic Web-based manufacturing resource discovery) for distributed manufacturing collaboration across ubiquitous virtual enterprises. Ontology-based annotation to the distributed manufacturing resources via a new, multidisciplinary manufacturing ontology is proposed on the semantic web to convert resources into machine understandable knowledge, which is a prelude to the meaningful resource discovery for cross-enterprise multidisciplinary collaboration. An ontology-based multi-level knowledge retrieval model is devised to extend the traditional information retrieval approaches based on keyword search, with integrated capabilities of graph search, semantic search, fuzzy search and automated reasoning to realise the intelligent discovery of manufacturing resources, e.g. to facilitate more flexible, meaningful, accurate and automated resource discovery. A case study for intelligent discovery of manufacturing resources is used to demonstrate the practicality of the developed system. [ABSTRACT FROM AUTHOR]

The development and maintenance of distributed collaborative manufacturing systems is faced with increasing challenges caused by the technical difficulties and ontological issues in distributed computing, resource integration and knowledge sharing over heterogeneous computing platforms. This paper presents an agent-based Semantic Grid for distributed manufacturing collaboration across ubiquitous virtual enterprises. A hierarchy of autonomous, adaptive agents forming a 'super peer' based peer-to-peer network is laid out in the Semantic Grid to provide, in an open, dynamic, loosely coupled and scalable manner, the service publication, discovery and reuse for management of applications that need to utilise Grid resources. The operation and experimental evaluation of the system are presented to validate the implementation of the proposed approach. [ABSTRACT FROM AUTHOR]

The development and maintenance of semantic web (SW) means that collaborative manufacturing systems are faced with increasing challenges caused by the growing difficulty in managing distributed manufacturing knowledge. This paper presents a multi-perspective modelling approach to systematically manage distributed manufacturing knowledge on the SW. Considering knowledge engineering as a cyclic and constructive modelling process, a multi-perspective knowledge modelling process is proposed to evolve along knowledge elicitation, engineering modelling, UML-based object modelling, OWL-based ontology modelling, knowledge formalisation and OWL-QL assisted knowledge verification activities. The proposed approach is viewed as a promising knowledge management method that facilitates the implementation of computer supported cooperative work (CSCW) in distributed manufacturing for SW applications by integrating the industrial, UML enabled software engineering techniques into recent ontology-based knowledge engineering process. The feasibility of knowledge management through multi-perspective modelling is manifested using the manufacturing ontology for manufacturing electronic connectors. [ABSTRACT FROM AUTHOR]