Multiscale analysis of re-entrant production lines: An equation-free approach

Y. Zou, I. G. Kevrekidis, Hans Armbruster

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


The computer-assisted modeling of re-entrant production lines, and, in particular, simulation scalability, is attracting a lot of attention due to the importance of such lines in semiconductor manufacturing. Re-entrant flows lead to competition for processing capacity among the items produced, which significantly impacts their throughput time (TPT). Such production models naturally exhibit two time scales: a short one, characteristic of single items processed through individual machines, and a longer one, characteristic of the response time of the entire factory. Coarse-grained partial differential equations for the spatio-temporal evolution of a "phase density" were obtained through a kinetic theory approach in Armbruster and Ringhofer [Thermalized kinetic and fluid models for re-entrant supply chains, SIAM J. Multiscale Modeling Simul. 3(4) (2005) 782-800.] We take advantage of the time scale separation to directly solve such coarse-grained equations, even when we cannot derive them explicitly, through an equation-free computational approach. Short bursts of appropriately initialized stochastic fine-scale simulation are used to perform coarse projective integration on the phase density. The key step in this process is lifting: the construction of fine-scale, discrete realizations consistent with a given coarse-grained phase density field. We achieve this through computational evaluation of conditional distributions of a "phase velocity" at the limit of large item influxes.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalPhysica A: Statistical Mechanics and its Applications
Issue number1
StatePublished - Apr 15 2006


  • Coarse projective integration
  • Equation-free
  • Production line
  • Re-entrant

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability


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