200
7 Summary and Outlook
e.g., a direct signal to the machine controller. Depending on the level of digitalization in production, this will only be feasible if Industry 4.0 machine and system
networking standards are already installed. Another problematic aspect regarding
the offset parameter is the consideration of machine failures in the simulation
model. The consideration of machine failures would add a stochastic to the model,
which requires the setup of replications for the single iterations of the optimization experiment, which can be done easily. But the occurrence of the machine
failures in the simulation run and the real production would never be the same
point in time. Therefore, in this work, failures are not considered, to be able to
detect all possible consumption peaks that might occur in practice and would
be hidden in the simulation model due to simulated failures. The disadvantage
of this procedure is, that once a failure actually occurs in the real production
line, the calculated offset parameter is not valid anymore, because in practice, the
energy consumption profiles that are added up to get the total consumption of the
production are different from what is simulated in the model.
Despite the limitations mentioned above, the advantages of using the
simulation-based optimization methodology to reduce the energy consumption
in production predominate. For researchers, there is a contribution to knowledge
in the fields of hybrid simulation and hybrid system modeling. At this point it is
emphasized that particularly the approach for creating the conceptual model for
hybrid simulations is scientifically relevant, since only a very few examples but
no established methods can be found in this area. The contribution of knowledge
for practical application lies in the generation of a step-by-step approach that
can be implemented in a multi-method simulation software without the need for
a simulation expert or programmer. The methodology can easily be applied for
single machines, production lines or whole production areas and is enlargeable
at any time in a project. In principle, only energy consumptions can be simulated that have previously been measured. Relationships between technological
production parameters (feed rate, processing speed, etc.) and the resulting energy
consumption are not modeled.
The presented research results demonstrate that the developed method is suitable for considering energy efficiency goals in traditional production simulation.
Thus, the research questions formulated at the beginning of the thesis can be
answered as follows:
Q1. How can the energy consumption of a production system be depicted in
a simulation model that can as well be used for optimization scenarios?
7 Summary and Outlook
e.g., a direct signal to the machine controller. Depending on the level of digitalization in production, this will only be feasible if Industry 4.0 machine and system
networking standards are already installed. Another problematic aspect regarding
the offset parameter is the consideration of machine failures in the simulation
model. The consideration of machine failures would add a stochastic to the model,
which requires the setup of replications for the single iterations of the optimization experiment, which can be done easily. But the occurrence of the machine
failures in the simulation run and the real production would never be the same
point in time. Therefore, in this work, failures are not considered, to be able to
detect all possible consumption peaks that might occur in practice and would
be hidden in the simulation model due to simulated failures. The disadvantage
of this procedure is, that once a failure actually occurs in the real production
line, the calculated offset parameter is not valid anymore, because in practice, the
energy consumption profiles that are added up to get the total consumption of the
production are different from what is simulated in the model.
Despite the limitations mentioned above, the advantages of using the
simulation-based optimization methodology to reduce the energy consumption
in production predominate. For researchers, there is a contribution to knowledge
in the fields of hybrid simulation and hybrid system modeling. At this point it is
emphasized that particularly the approach for creating the conceptual model for
hybrid simulations is scientifically relevant, since only a very few examples but
no established methods can be found in this area. The contribution of knowledge
for practical application lies in the generation of a step-by-step approach that
can be implemented in a multi-method simulation software without the need for
a simulation expert or programmer. The methodology can easily be applied for
single machines, production lines or whole production areas and is enlargeable
at any time in a project. In principle, only energy consumptions can be simulated that have previously been measured. Relationships between technological
production parameters (feed rate, processing speed, etc.) and the resulting energy
consumption are not modeled.
The presented research results demonstrate that the developed method is suitable for considering energy efficiency goals in traditional production simulation.
Thus, the research questions formulated at the beginning of the thesis can be
answered as follows:
Q1. How can the energy consumption of a production system be depicted in
a simulation model that can as well be used for optimization scenarios?