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  • The wide spread availability of real-time plant floor systems (PFS) information has made the modern automotive assembly line a data-rich environment. Information from these data sources offers an unprecedented opportunity to model and simulate the performance of an assembly line as a dynamic system, as opposed to a conventional static manner. The dynamic models in turn can enable fast and accurate prediction of aggregate performance in multistage assembly line operations. This paper presents a data-driven continuous fluid flow approach, founded on nonlinear system dynamics (SD) principles, to model assembly line dynamics. The movement of entities is treated as a fluid flow, buffer stocks are water tanks, the conveyor belt is water pipe and manufacturing stations are the valves which control the rates of flow. A set of ordinary differential equations (ODE) is derived to model the system of buffer stocks and production flows between the interacting machines. The proposed continuous flow models are implemented in Matlab's Simulink environment with real-world data from a production line segment of 18 machines. The results show that the instantaneous (i.e. approximately 1 week of actual operation time) throughput rate values from the continuous flow model were within 5% of the historical data averages while the results from an equivalent discrete event simulation (DES) model were inferior. In addition, the steady-state results of a 20-h simulation run (i.e. approximately 1 year of actual operation time) match well between the DES Model and the continuous flow model. This investigation is strongly indicative of the potential use of continuous flow models to capture the aggregated assembly line dynamics and yield deeper insights into the interrelations between the different parts of a complex manufacturing system. 2013 Taylor & Francis Group, LLC.

published proceedings

  • International Journal of Computer Integrated Manufacturing

author list (cited authors)

  • Yang, H., Bukkapatnam, S., & Barajas, L. G.

publication date

  • January 1, 2013 11:11 AM