An outsourcing service selection method using ANN and SFLA algorithms for cement equipment manufacturing enterprises in cloud manufacturing

In cloud manufacturing (CMfg) environments, an increasing number of manufacturing enterprises outsource manufacturing activities to subcontractors that are more professional to focus on the development of their core business. Volume, diversity, and variety are the typical characteristics of outsourcing services for cement equipment manufacturing enterprises (CEMEs). To address the new problem of service discovery and combinatorial optimization of outsourcing resources (COOR), a novel heuristic approach is investigated in this paper. First, a clustering and searching model of a web outsourcing service based on Ontology Web Language for Services (OWL-S) and an artificial neural network (ANN) is established. Then, an improved shuffled frog leaping algorithm (SFLA) is developed to solve the COOR problem. Finally, an investigation and comparative experiments based on a group of cement equipment manufacturing companies is presented. The experimental results show that the proposed method is preferable and is more efficient for solving large-scale problems in a CMfg environment.