Stochastic dynamics of long supply chains with random breakdowns

被引：20
|
作者：
Degond, P.
Ringhofer, C.
机构：
[1] Univ Toulouse 3, Lab CNRS, UMR, MIP, F-61062 Toulouse, France
[2] Arizona State Univ, Dept Math, Tempe, AZ 85287 USA
来源：
SIAM JOURNAL ON APPLIED MATHEMATICS | 2007年 / 68卷 / 01期
关键词：
supply chains; traffic flow models; mean field theories; Boltzmann equation; fluid limits; NETWORK MODELS;
D O I：
10.1137/060674302
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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.
引用
收藏
页码：59 / 79
页数：21
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