Stochastic dynamics of long supply chains with random breakdowns

P. Degond; Christian Ringhofer

doi:10.1137/060674302

Stochastic dynamics of long supply chains with random breakdowns

P. Degond, Christian Ringhofer

CLAS-NS: Mathematics and Statistical Sciences, School of (SMSS)

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	59-79
Number of pages	21
Journal	SIAM Journal on Applied Mathematics
Volume	68
Issue number	1
DOIs	https://doi.org/10.1137/060674302
State	Published - 2007

Keywords

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

ASJC Scopus subject areas

Applied Mathematics

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Stochastic dynamics of long supply chains with random breakdowns

Abstract

Keywords

ASJC Scopus subject areas

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