An approach to IoT service optimal composition for mass customization on cloud manufacturing

Tianyang Li, He Ting, Zhongjie Wang, Yufeng Zhang

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
140 Downloads (Pure)

Abstract

For implementing mass customization on cloud manufacturing (CMfg), Internet of Things (IoT)-enabled service optimal composition (ISOC) is a key technology used to effectively composite a manufacturing cloud service with selected IoT services to satisfy the users’ customized production requirements. The solving of the ISOC problem is done inefficiently in the context of the growing scale of IoT services and increasing sophistication of the ISOC execution path, being partly due to insufficient investigation of accumulated empirical knowledge (EK) of IoT services. In this paper, we propose an EK-oriented genetic algorithm (EK-GA) for the large-scale IoT service composition. First, by fully con- sidering the distinctive features of IoT service and service domain features, the EK of IoT services are richly explored to divide the service space. Second, EK-oriented optimization strategies in the initial population, operators, and fitness function are presented to improve the local and global search abilities of the genetic algorithm for solving ISOC problems. Finally, the effectiveness of EK-GA for solving real-world ISOC problems in a private CMfg is verified through three types of experiments. By exploiting EK of IoT services for ISOC problems, this work makes novel contributions for mass customization on CMfg and enriches the practice of EK-oriented intelligence optimization.
Original languageEnglish
Number of pages16
JournalIEEE Access
Volume8
Early online date10 Sep 2018
DOIs
Publication statusPublished - 4 Oct 2018

Keywords

  • IoT services
  • service composition
  • mass customization
  • cloud manufacturing

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