4.8 Article

An Optimal Production Scheme for Reconfigurable Cloud Manufacturing Service System

Journal

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 12, Pages 9037-9046

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2022.3169979

Keywords

Manufacturing; Production; Cloud computing; Resource management; Costs; Mathematical models; Analytical models; Cloud manufacturing (CMfg); personalized customization; resource allocation; rewritable Petri nets (RPN)

Funding

  1. Major Science and Technology Innovation Project of Shandong Province [2019TSLH0214]
  2. Tai Shan Industry Leading Talent Project [tscy20180416]
  3. Fundamental Research Funds for the Central Universities [20CX05016A]
  4. Major Scientific and Technological Projects of CNPC [ZD2019-183-007]
  5. graduate innovation projects of China University of Petroleum (East China) [YCX2020097]

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Cloud manufacturing platform is a service model that provides solutions for large-scale personalized customization, but the service flexibility and resource allocation constraints affect the production time and cost. This article proposes a service model based on rewritable Petri nets and a resource allocation strategy based on nondominated sorting genetic algorithm to obtain the optimal personalized customization scheme in terms of time and cost.
Cloud manufacturing (CMfg) platform consists of the cloud services, manufacturing technology, and the Internet of Things, which provides solutions for large-scale personalized customization through the service model. However, the service flexibility and resource allocation of CMfg are two factors that restrict the production time and cost of CMfg. A CMfg service model based on rewritable Petri nets (RPNs) is established, where the reconfiguration process of personalized customization is described by the rewritable rules of RPN. On this basis, the performance of the reconfiguration of the personalized customization service process is analyzed (this model analysis method can analyze the soundness of the reconfiguration process). In addition, we establish the resource allocation strategy of CMfg based on nondominated sorting genetic algorithm to obtain the best personalized customization scheme in terms of time and cost. The results of simulation and comparison experiments show that the method proposed in this article can obtain the optimal solution for both production time and cost.

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