Semantic-Based Reasoning Engine for Service Orchestration in Mass Customization Manufacturing

Abstract:
In the mass customization manufacturing environment, manufacturing companies can connect to the customers directly via Internet and receive users manufacturing request to adjust the production resource and patterns. In this new situation, the allocation of manufacturing resources and the adjustment of production configuration should have the capability of timely responsing the demands from a large amount of customers. Therefore, efficient matching and processing of dynamic data from customer requirement and customized rules become increasingly important. This paper presents a semantic- based reasoning engine (hereinafter referred to as SBRE) in improving performance of rule engine. The paper uses a BP neural network to classify customer parameters from users requirements based on the semantic web interface. In this stage, for the customer parameters, in order to find a suitable rule engine, a semantic matching method is proposed, which can greatly mitigate complexity and improve the efficiency of matching compared with traditional matching process. The experiment results show that the method can be performed efficiently and flexibly with high data throughput.