A Two-stage Optimization Method of Manufacturing Service Composition for Personalized Customers

With the development of manufacturing globalization, service-oriented manufacturing mode promotes customers to configure the manufacturing service composition online to meet personalized manufacturing needs. Personalized manufacturing service composition optimization can greatly enhance customer friendliness and convenience. However, existing researches on manufacturing service composition optimization are difficult to comprehensively weigh customer preferences and the quality of the service (QoS) attributes of the service composition. Therefore, this paper proposes a two-stage optimization model for personalized manufacturing service composition, the two-stage optimization includes customer preference-based optimization for candidate manufacturing services and multi-objective optimization for personalized manufacturing service composition. To solve the model, a hybrid NSGA-II&CF algorithm is proposed. The collaborative filtering (CF) is applied to quantifying the customer preferences of candidate manufacturing services, the NSGA-II is employed for the multi-objective optimization of personalized manufacturing service composition. Finally, comparison experiments are provided to verify the effectiveness of the proposed NSGA-II&CF algorithm.