Data quality management for service-oriented manufacturing cyber-physical systems. (November 2017)

• Record Type: Journal Article
• Authors: Song, Zhiting; Sun, Yanming; Wan, Jiafu; Liang, Peipei
• Is Part Of: Computers & electrical engineering. Volume 64(2017)
• Page Start: 34
• Page End: 44
• Publication Date: 2017-11
• Subjects: Data quality -- Cyber-physical systems -- Service-oriented manufacturing -- Workflow nets; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.08.010
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Process-oriented ontology for a SMCPS is established based on WF-nets. A two-stage optimization model for data quality management of a SMCPS is proposed. Two algorithms to solve the two-stage optimization model are designed. The advantages of the proposed model are significant in balancing local and global management objectives. Abstract: Service-oriented manufacturing (SOM) is a new worldwide manufacturing paradigm, and a cyber-physical system (CPS) is accepted as a strategic choice of SOM enterprises looking to provide bundles of satisfying products and services to customers. The issue of data quality is common in any CPS and poses great challenges to its efficient operation. This paper focuses on defective data generated by the improper operation of physical and cyber components of a service-oriented manufacturing CPS (SMCPS), and develops effective managerial policies to deal with such data. First, formal semantics of workflow nets (WF-nets) are employed to construct process-oriented ontology for the SMCPS. Second, a two-stage optimization model together with algorithms is designed to find optimal policies that balance local and global management objectives. Finally, our model is illustrated through a case. Results show that the proposed control strategy outperforms one-stage control and random control in guaranteeing data quality and saving control costs. Graphical abstract: