A two-layer social network model for manufacturing service composition based on synergy: A case study on an aircraft structural part. (October 2020)

• Record Type: Journal Article
• Authors: Tong, Huagang; Zhu, Jianjun
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 65(2020)
• Publication Date: 2020-10
• Subjects: Cloud manufacturing -- Synergy -- Prospect theory -- Bi-level programming; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2020.101933
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Cloud manufacturing is an emerging manufacturing paradigm, with the issue of service composition being one of its most significant challenges. The current researches take the view that all manufacturing services are of equal importance. However, in fact, manufacturing services that owns scarce resources are more important than ordinary one, especially in the production of complex products. Hence, it is significant to meet the requirements of manufacturing services that own scarce resources first. Meanwhile, due to the increasing complexity of products, a high degree of synergy is required in service composition. Our study explores a new framework of social relationships from the perspective of synergy. A two-layer social network model is first proposed for resource allocation that takes synergy and priority into account. The upper layer of this model is occupied by the manufacturing services with scare resources. The lower layer is occupied by ordinary manufacturing services. All services are connected by social relationships. For the master-slave characteristic of the model, the two-layer network is embedded into bi-level programming. Then, an improved genetic algorithm is designed to solve the problem. Finally, the effectiveness of the model and algorithm are verified using a structural part of the Modern Ark 60 as an example.