New peer effect-based approach for service matching in Cloud Manufacturing under uncertain preferences

Abstract

Cloud manufacturing is a kind of sharing manufacturing, and the supply–demand matching between manufacturing services and customers has become one of the most important issues for a platform. Because of the increasing complexity of customer personalization, the cognitive information from both sides becomes uncertain and fuzzy. Linguistics is used to describe uncertain preferences, especially in platforms. Also, the cloud model is adopted to convert the linguistics to reflect the randomness and fuzziness. Meanwhile, as the rapid development of communication techniques has strengthened the connections between different agents, the final matching results are affected by connections. Hence, the peer effect, which describes the mutual influences among individuals, is introduced in our study. In addition, considering the different strengths of the connections, the peer effect is improved by integrating grey relations, which are used to evaluate the diverse connections. Finally, the workload is introduced in the form of adjustment parameters. Consequently, we establish a bi-objective model that aims to maximize satisfaction and minimize the differences among individuals. To solve the mathematical model, an improved cuckoo algorithm is proposed. Additionally, the design of an air outlet grille for new-energy vehicles is taken as an example, and the effectiveness of the proposed method is verified.