Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system. (October 2017)

• Record Type: Journal Article
• Authors: Lin, Ting Yu; Yang, Chen; Zhuang, Changhui; Xiao, Yingying; Tao, Fei; Shi, Guoqiang; Geng, Chao
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 231:Number 12(2017)
• Page Start: 2159
• Page End: 2172
• Publication Date: 2017-10
• Subjects: Cloud manufacturing -- resource management -- resource allocation -- distributed scheduling -- multi-centric -- manufacturing capability; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405415624364
• Languages: English
• ISSNs: 0954-4054

Abstract

Cloud manufacturing offers the potential to make mass manufacturing resources and capabilities more widely integrated and accessible to users through network. Most related research assumes that there exists only one management center for all manufacturing resources and capabilities in a manufacturing cloud. However, this could cause the efficiency problem (e.g. scheduling time) and harm the quality of service (e.g. response time). Actually, a large-scale manufacturing cloud should have multiple management centers to deal with massive, widely distributed manufacturing resources and capabilities and users; meanwhile, the constraint of finite manufacturing resources and capabilities and the cost of remote collaboration should be taken into consideration. Thus, this article first presents the architecture for the multi-centric management with two-level scheduling strategy combining the advantages of the centralized and decentralized decision-making. Then, after quantifying the availability and the collaborative cost of the manufacturing resources and capabilities, we propose a global optimization model for the manufacturing resources and capability allocation under the multi-centric architecture. Finally, a case study adopting our new method shows that the utilization of the manufacturing resources and capabilities would be more balanced, while the cost of the total collaboration would be reduced, compared with the typical decentralized solution. The research results can support cloud manufacturing to effectively deal with the challenge of management and allocation for increasingly large-scale and distributed manufacturing resources and capabilities.