Stochastic dynamics of long supply chains with random breakdowns

P. Degond, Christian Ringhofer

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


We analyze the stochastic large time behavior of long supply chains via a traffic flow random particle model. As items travel on a virtual road from one production stage to the next, random breakdowns of the processors at each stage are modeled via a Markov process. The result is a conservation law for the expectation of the part density which holds on time scales which are large compared to the mean up and down times of the processors.

Original languageEnglish (US)
Pages (from-to)59-79
Number of pages21
JournalSIAM Journal on Applied Mathematics
Issue number1
StatePublished - 2007


  • Boltzmann equation
  • Fluid limits
  • Mean field theories
  • Supply chains
  • Traffic flow models

ASJC Scopus subject areas

  • Applied Mathematics


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