152
5 Development of a Simulation-based Methodology …
5.6.6 Conclusions for the Practical Application
of the Methodology
Through different simulation experiments with a manually varied machine schedule, batch size variations for individual machines of the production line and the
deliberate avoidance of non-value-added machine states, it was possible to test
how the energy consumption of the production can be reduced without negatively
affecting the total daily output. The simulated energy profiles of the machines in
the fictional case study depict the dynamic behavior of the energy consumption
and even the consideration of single machines using mean values instead of exact
load profiles was successfully tested.
By treating the peak optimization objective as a restriction in the total consumption optimizer, it was tried to summarize all objective functions in only one
optimization experiment. The solutions achieved by this were not better, as neither the total consumption nor the peak reduction was more successful. In terms
of occurring peak loads, the individual execution of the optimization experiments
led to the same results. However, the equal consideration of the consumption
peaks has led to experiments with poorer total energy consumption and their
optimization parameters being presented as ideal solutions.
As the reduction of total energy consumption is the number one objective, this
optimizer should be run first. Afterwards, the peaks for this ideal solution should
be optimized again to get not only the solution with the lowest total energy consumption but to optimize the occurring peaks for this low consumption parameter
set. Therefore, the experiments have to be separated and are run in the above defined order. This is referred to as lexicographical ordering 14 of objective functions
[Br2008, p. 19; NM2005, p. 14].
While the fictional example describes a production line in which the individual machines are rather loosely dependent on one another and are decoupled
from each other by smaller buffers, the practical case in chapter 6 depicts two
production lines with strongly connected production machines. The failure of one
machine in the production line leads to a downtime of the entire line with a
minimum offset of a few seconds between the single machines. For this reason,
14 “In lexicographic ordering […], [the decision maker] must arrange the objective functions
according to their absolute importance. This means that a more important objective is infinitely more important than a less important objective. After the ordering, the most important
objective function is minimized subject to the original constraints. If this problem has a unique solution, it is the final one and the solution process stops. Otherwise, the second most
important objective function is minimized. Now, a new constraint is introduced to guarantee
that the most important objective function preserves its optimal value” [Br2008, p. 19].
5 Development of a Simulation-based Methodology …
5.6.6 Conclusions for the Practical Application
of the Methodology
Through different simulation experiments with a manually varied machine schedule, batch size variations for individual machines of the production line and the
deliberate avoidance of non-value-added machine states, it was possible to test
how the energy consumption of the production can be reduced without negatively
affecting the total daily output. The simulated energy profiles of the machines in
the fictional case study depict the dynamic behavior of the energy consumption
and even the consideration of single machines using mean values instead of exact
load profiles was successfully tested.
By treating the peak optimization objective as a restriction in the total consumption optimizer, it was tried to summarize all objective functions in only one
optimization experiment. The solutions achieved by this were not better, as neither the total consumption nor the peak reduction was more successful. In terms
of occurring peak loads, the individual execution of the optimization experiments
led to the same results. However, the equal consideration of the consumption
peaks has led to experiments with poorer total energy consumption and their
optimization parameters being presented as ideal solutions.
As the reduction of total energy consumption is the number one objective, this
optimizer should be run first. Afterwards, the peaks for this ideal solution should
be optimized again to get not only the solution with the lowest total energy consumption but to optimize the occurring peaks for this low consumption parameter
set. Therefore, the experiments have to be separated and are run in the above defined order. This is referred to as lexicographical ordering 14 of objective functions
[Br2008, p. 19; NM2005, p. 14].
While the fictional example describes a production line in which the individual machines are rather loosely dependent on one another and are decoupled
from each other by smaller buffers, the practical case in chapter 6 depicts two
production lines with strongly connected production machines. The failure of one
machine in the production line leads to a downtime of the entire line with a
minimum offset of a few seconds between the single machines. For this reason,
14 “In lexicographic ordering […], [the decision maker] must arrange the objective functions
according to their absolute importance. This means that a more important objective is infinitely more important than a less important objective. After the ordering, the most important
objective function is minimized subject to the original constraints. If this problem has a unique solution, it is the final one and the solution process stops. Otherwise, the second most
important objective function is minimized. Now, a new constraint is introduced to guarantee
that the most important objective function preserves its optimal value” [Br2008, p. 19].