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A service-oriented simulation integration platform for hierarchical manufacturing planning and control

Author

Listed:
  • Dong Xu
  • Sai Srinivas Nageshwaraniyer
  • Young-Jun Son

Abstract

In this paper, a coherent and comprehensive distributed simulation platform is proposed to support hierarchical manufacturing planning and control. This platform enables integration of various hardware and software components within and across supply chain members, such as manufacturing equipment, physics-based process simulators, system-dynamic, agent-based and discrete-event simulators (DESs), and databases via web services technology. At the shop level, a DES model may run in a stand-alone mode, or in conjunction with other simulators (e.g. process or agent-based simulators) and/or hardware (e.g. robots and machines). At the enterprise level, multiple shop floor simulators (each representing a factory) are integrated into a geographically dispersed environment. Exemplary models simulating aircraft drill and assembly on the shop floor are developed in widely used packages, such as Arena®, Simio®, Anylogic® and ESPRIT®, and then integrated. The proposed platform with the hardware-in-the-loop capability is successfully implemented and demonstrated for an automated manufacturing cell in the Computer Integrated Manufacturing and Simulation lab at the University of Arizona. Experiments are conducted using the proposed platform to test (1) reduction in estimated variance of part cycle time given variations in order arrival rate, effectiveness of materials processers and material handlers, and (2) computational time performance of web services. The experimental results reveal that the proposed platform is viable to enable both vertical (shop simulator to equipment level process simulators and/or equipment) as well as horizontal (e.g. multiple shop level simulators) integrations.

Suggested Citation

  • Dong Xu & Sai Srinivas Nageshwaraniyer & Young-Jun Son, 2016. "A service-oriented simulation integration platform for hierarchical manufacturing planning and control," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7212-7230, December.
  • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:23:p:7212-7230
    DOI: 10.1080/00207543.2016.1221535
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