5.3 Description of the M&S Approach
101
Figure 5.7 Different complexity types of the machine logic
The production machines go through different operating states, whose time
sequence and duration are affected and determined by technical requirements on
the one hand and dependent on production quantities and time tables on the other.
An exception is the failure state, which is usually entered unplanned. As machine
failures are, unless they have a significant effect on the production performance,
often overlooked when modeling a manufacturing system, Rohrer proposes four
options to handle machine downtimes. They can either be ignored, which is probably the most chosen variant, they can be included by adjusting processing times,
considered as constant values for time-to-failure and time-to-repair, or as a fourth
option, statistical distributions 2 for time-to-failure and time-to-repair can be used
[Ro1998, pp. 525–526].
The energy requirements of the individual operating states of a machine can
vary greatly, both in terms of height and curve progression. While some operating
states might rather be constant over a longer period of time, others have a highly
volatile power consumption profile. Some operating states, e.g., the warm up and
setup state, have a defined length which is technically required to set the machine
ready for production, while for others the retention time of a machine in that
2 Random failure behavior can be described by different probability distributions. “The extensive use of the Weibull distribution in the interpretation and analysis of failure phenomena is
mainly related to the fact that the shape of its failure rate curve depends on a single parameter” [TB2017, p. 51]. The interested reader is referred to the further literature of the authors
Bertsche, Schauz, and Pickard and Trivedi and Bobbio [BSP2011, pp. 40–54; TB2017,
pp. 46–65].
101
Figure 5.7 Different complexity types of the machine logic
The production machines go through different operating states, whose time
sequence and duration are affected and determined by technical requirements on
the one hand and dependent on production quantities and time tables on the other.
An exception is the failure state, which is usually entered unplanned. As machine
failures are, unless they have a significant effect on the production performance,
often overlooked when modeling a manufacturing system, Rohrer proposes four
options to handle machine downtimes. They can either be ignored, which is probably the most chosen variant, they can be included by adjusting processing times,
considered as constant values for time-to-failure and time-to-repair, or as a fourth
option, statistical distributions 2 for time-to-failure and time-to-repair can be used
[Ro1998, pp. 525–526].
The energy requirements of the individual operating states of a machine can
vary greatly, both in terms of height and curve progression. While some operating
states might rather be constant over a longer period of time, others have a highly
volatile power consumption profile. Some operating states, e.g., the warm up and
setup state, have a defined length which is technically required to set the machine
ready for production, while for others the retention time of a machine in that
2 Random failure behavior can be described by different probability distributions. “The extensive use of the Weibull distribution in the interpretation and analysis of failure phenomena is
mainly related to the fact that the shape of its failure rate curve depends on a single parameter” [TB2017, p. 51]. The interested reader is referred to the further literature of the authors
Bertsche, Schauz, and Pickard and Trivedi and Bobbio [BSP2011, pp. 40–54; TB2017,
pp. 46–65].