Determistic simplifications of stochastic systems in SD
Posted: Wed Dec 03, 2003 9:17 am
Dear List readers,
I am a post-graduate student at Helsinkin Universty of Technology. My doctoral
dissertation will be reviewed and accepted in February. In the disseratation I
present two SD models: one for industrial maintenance and repair, and one for
spare parts supply chain. From the initial comments I know that there will be
questions about the validity of the models.
The maintenance model includes component wear and degradiation model (known as
hazard curve) as a function of time. The aim is to model the behavior of the
maintenance system and component reliability. Usually, the maintenance
simulation and degradiation has been handled with stochastic discrete event
monte-carlo simulation. Also, the maintenance and repair times (delays) are
usually modelled with stochastic distributions (e.g. gamma, normal). The
benefit of this approach is that the distributions have distributions-specific
deviations and the simulation really generates these values randomly.
Now the question is, can I handle these events (failures & maintenance) with
their average values (e.g. failure_rate(t)= hazard_rate(t) and
maintenance_delay=4 weeks)? And, are there any relation between 3rd order
delay and probabilistic distributions?
If anyone has seen this issue been analyzed I would appreciate any links,
references and comments.
Br,
Tuomo Honkanen
Helsinki University of Technology
tuomo.honkanen@hut.fi
I am a post-graduate student at Helsinkin Universty of Technology. My doctoral
dissertation will be reviewed and accepted in February. In the disseratation I
present two SD models: one for industrial maintenance and repair, and one for
spare parts supply chain. From the initial comments I know that there will be
questions about the validity of the models.
The maintenance model includes component wear and degradiation model (known as
hazard curve) as a function of time. The aim is to model the behavior of the
maintenance system and component reliability. Usually, the maintenance
simulation and degradiation has been handled with stochastic discrete event
monte-carlo simulation. Also, the maintenance and repair times (delays) are
usually modelled with stochastic distributions (e.g. gamma, normal). The
benefit of this approach is that the distributions have distributions-specific
deviations and the simulation really generates these values randomly.
Now the question is, can I handle these events (failures & maintenance) with
their average values (e.g. failure_rate(t)= hazard_rate(t) and
maintenance_delay=4 weeks)? And, are there any relation between 3rd order
delay and probabilistic distributions?
If anyone has seen this issue been analyzed I would appreciate any links,
references and comments.
Br,
Tuomo Honkanen
Helsinki University of Technology
tuomo.honkanen@hut.fi