Search

Cloud manufacturing (CMfg) is emerging as an advanced service-oriented manufacturing model for the manufacturing industry. It is changing the way manufacturing enterprises organize production activities in that tangible and intangible manufacturing resources encapsulated as virtualized manufacturing services are provided to customers. In this background, CMfg task semantic modeling and description is proposed to address different challenges such as matching with manufacturing services. Firstly, some of the essential features of CMfg tasks are briefly discussed and classification as well as the modeling requirements of CMfg tasks is also investigated, and then this paper proposes the modeling framework of CMfg task to solve the general CMfg task ontology (GCMT_Ontology) construction and the task sub-ontology matching from GCMT_Ontology. In GCMT_Ontology construction process, the original CMfg task ontology (OCMT_Ontology) is built from the CMfg task description model, and then an ontology learning approach is advanced to perfect the GCMT_Ontology with graph-based semantic similarity algorithm. In task sub-ontology matching process, semantic feature values of concepts are calculated to extend the semantic content of the task document, and the extended concepts are input to find the sub-ontology by computing minimum connected sub-graph. Finally, the proposed framework is realized in a prototype system, and an example of cement grinding mill assembling task is carried out in a group enterprise to illustrate the application. The results show that the proposed method improves enterprise collaboration and interoperation effectively. [ABSTRACT FROM AUTHOR]

We study a joint replenishment and delivery scheduling (JRD) problem in which a central warehouse serves n-retailers in the presence of vague operational conditions such as ordering cost and inventory holding cost. In the proposed fuzzy set-based approach, an exact membership function is not assumed and instead can be approximated using piecewise linear functions based on alpha level sets because of their easy handling and efficiency. Subsequently, the fuzzy total cost is defuzzified by the widely used signed distance method to ranking fuzzy numbers. However, due to the JRD's difficult mathematical properties, efficient and effective solution procedures for the problem have eluded researchers. To find an optimal solution, an effective and efficient differential evolution (DE) algorithm is designed. After determining the appropriate parameters of the DE by parameter tuning test, the effectiveness of the DE is verified by numerical examples. We compare the DE with the available best approach and results show that DE can solve this non-deterministic polynomial hard problem in a robust way with a high convergence rate and low average error. [ABSTRACT FROM AUTHOR]