Cloud manufacturing service recommendation model based on GA-ACO and carbon emission hierarchy

Article type: Research Article

Authors: Shi, Zhihu^{; *}

Affiliations: Business School, Hohai University, Nanjing, China

Correspondence: [*] Corresponding author. Zhihu Shi. E-mail: [email protected].

Abstract: In order to improve the accuracy of cloud manufacturing service recommendation results, improve recommendation efficiency and user satisfaction, a cloud manufacturing service recommendation model based on GA-ACO and carbon emission hierarchy is proposed. According to the concept of cloud manufacturing, a cloud manufacturing platform including resource layer, service layer, operation layer and application layer is constructed, and then a cloud manufacturing service quality perception model is established; genetic algorithm is used to realize cloud manufacturing service selection, and ACO algorithm is used to optimize cloud manufacturing service portfolio; According to the selection and combination results of the constructed cloud manufacturing platform and cloud manufacturing service, taking the carbon emission field as an example, a hierarchical hierarchical model is constructed, and this model is used to further construct a cloud manufacturing service recommendation model from coarse to fine, from global to local; Identify user demand scenarios and implement cloud manufacturing service recommendations. The experimental results show that the recommendation results of the proposed method have high accuracy and efficiency, and can be recognized by most users.

Keywords: GA-ACO, carbon emission hierarchy, service recommendation, quality perception model, cloud manufacturing platform

DOI: 10.3233/JIFS-222386

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2007-2017, 2023