Two-dimensional optimization mechanism and method for on-demand supply of manufacturing cloud service. (March 2018) - Huang, Shenquan; Gu, Xinjian; Zhou, Hongming; Chen, Yarong
Highlights: Coupling exists among individuation, cost and response time in cloud manufacturing. The principle of user demand decoupling point is proposed. A 2D optimization model is established for describing the optimization mechanism. A time–space 2D optimization method is proposed for decoupling the contradiction. Case studies in a developed system are verified the optimization model and method. Abstract: The on-demand supply mode of cloud manufacturing is significant in enlivening the manufacturing resources over a wide range. However, problems in coupling and contradiction exist among individuation, cost and response time in the on-demand supply of manufacturing cloud service, thus decreasing the efficiency and effectiveness of cloud manufacturing. To decouple this contradiction problem and help users rapidly obtain individuation services at low cost, this paper proposes a two-dimensional (time and space dimensions) optimization mechanism and method for manufacturing cloud service. In the mechanism layer, the user demand decoupling point principle, which is formulated using the customer order decoupling point in traditional physical products, is proposed for describing the optimization direction; and the on-demand supply mode of manufacturing cloud service is divided into four sub-modes, i.e., provide-to-demand, compose-to-demand, engineer-to-demand and research-to-demand; then, a two-dimensional optimization model is established for describing the optimization mechanism among individuation, cost and response time. In the method layer, the decision-making process model for positioning and optimizing UDDP is studied to support the time dimension optimization of moving UDDP backward; and a recursive mapping model of the service topological structure is proposed for supporting the space dimension optimization of the service topological structure. Finally, the proposed model and method are applied to a cloud manufacturing service platform for the mold industry. Two case studies are presented for demonstrating the feasibility and effectiveness of this two-dimensional optimization approach.