2
1 Introduction
consumption, as the production is directly linked to the use of resources and
immediately affected by shortages and changes [Ne+2008, p. 2].
As the use of simulation technology has become a common tool to model,
analyze, and assess dynamic production processes [St+2006, p. 394], the consideration of energy-related issues in the context of simulation is becoming a more
frequent subject in scientific discussions. The integration of auxiliary and ancillary
processes to support a holistic view of the value chain in a production unit requires
new or modified simulation approaches [SWM2014, p. 71]. Existing approaches
mainly focus on the consideration of resource consumption variables based on
metrologically collected data on operating states, which are considered to be constant over a certain period of time. The energy consumption of machines is defined
to be status-based and can be modeled and simulated using discrete event simulation (DES) approaches. Additionally, the system borders within the different
scientific approaches to optimize the overall energy consumption of a production
are defined in various ways and range from the consideration of machines that
are directly involved in production processes to the consideration of compressed
air connections and peripheral facilities. The prevailing majority of approaches
does not consider all consumers that contribute a share to the overall energy consumption for the model definition, as the efforts for the data acquisition to model
the operating states are too high. For the modeling and simulation of highly dynamic production processes DES approaches do often not provide a sufficient model
accuracy to simulate such processes because they use quasi-static operating states.
The latest scientific publications state the approach of hybrid simulation as a possible solution for a realistic representation of highly dynamic processes [SP2014,
p. 109; Ba+2017, p. 67; SKS2017, p. 440; Pe+2017, p. 3792]. Using hybrid simulation could comprise the utilization of continuous simulation approaches, which
are generally used for the modeling of physical processes, such as the behavior of
liquids and gases, for the depiction of the total energy demand and combining it
with a DES approach for the modeling of material flows and supporting logistic
processes. By merging both models, the complex interactions between the material flow and the energy usage in production can be simulated closer to reality,
especially the depiction of energy consumption peaks could become possible.
An essential step towards reducing energy consumption in production is the
optimization of the energy use of non-value-adding production phases. In these
phases, the production equipment unnecessarily consumes energy but is not
actively used to produce and create value. Companies can address this waste
of resources without the need for large investments. Optimizing non-valueadding machine times thus harbors an enormous potential, whose development
in industrial practice currently lacks the necessary planning tools. To capture all
1 Introduction
consumption, as the production is directly linked to the use of resources and
immediately affected by shortages and changes [Ne+2008, p. 2].
As the use of simulation technology has become a common tool to model,
analyze, and assess dynamic production processes [St+2006, p. 394], the consideration of energy-related issues in the context of simulation is becoming a more
frequent subject in scientific discussions. The integration of auxiliary and ancillary
processes to support a holistic view of the value chain in a production unit requires
new or modified simulation approaches [SWM2014, p. 71]. Existing approaches
mainly focus on the consideration of resource consumption variables based on
metrologically collected data on operating states, which are considered to be constant over a certain period of time. The energy consumption of machines is defined
to be status-based and can be modeled and simulated using discrete event simulation (DES) approaches. Additionally, the system borders within the different
scientific approaches to optimize the overall energy consumption of a production
are defined in various ways and range from the consideration of machines that
are directly involved in production processes to the consideration of compressed
air connections and peripheral facilities. The prevailing majority of approaches
does not consider all consumers that contribute a share to the overall energy consumption for the model definition, as the efforts for the data acquisition to model
the operating states are too high. For the modeling and simulation of highly dynamic production processes DES approaches do often not provide a sufficient model
accuracy to simulate such processes because they use quasi-static operating states.
The latest scientific publications state the approach of hybrid simulation as a possible solution for a realistic representation of highly dynamic processes [SP2014,
p. 109; Ba+2017, p. 67; SKS2017, p. 440; Pe+2017, p. 3792]. Using hybrid simulation could comprise the utilization of continuous simulation approaches, which
are generally used for the modeling of physical processes, such as the behavior of
liquids and gases, for the depiction of the total energy demand and combining it
with a DES approach for the modeling of material flows and supporting logistic
processes. By merging both models, the complex interactions between the material flow and the energy usage in production can be simulated closer to reality,
especially the depiction of energy consumption peaks could become possible.
An essential step towards reducing energy consumption in production is the
optimization of the energy use of non-value-adding production phases. In these
phases, the production equipment unnecessarily consumes energy but is not
actively used to produce and create value. Companies can address this waste
of resources without the need for large investments. Optimizing non-valueadding machine times thus harbors an enormous potential, whose development
in industrial practice currently lacks the necessary planning tools. To capture all