2
Simulation-Based Optimization
There are things we can do to combat complexity. We
must use all the resources at our disposal to fight the
complexity dragon. At the moment, he’s winning.
JAMES O. Henriksen(Henriksen at the Titan’s Talk of
the Winter Simulation Conference 2006 [He2006, p. 2].)
“Simulation refers to a broad collection of methods and applications to mimic
the behavior of real systems, usually on a computer with appropriate software”
[KSZ2002, p. 3]. The use of simulation is a proven decision support tool in operational practice for production planning and control, whenever a complex system
of target figures, control parameters and disturbance variables are present, and the
number of system components results in a complex system behavior over time.
It is hardly feasible to handle this complexity by analytical methods and to give
tractable mathematical formulations [MW2011, p. 7]. Through the simulation, it
becomes possible to depict system-inherent causal relationships and to calculate
the result variables based on the dynamic runtime behavior. Simulation is a high
value tool but is not fully sufficient by itself. “An extra step is needed—a step that
joins simulation and optimization” [Ap+2004, p. 76]. This thesis uses modeling
and simulation techniques as a tool to depict the energy consumption as well as
its complex interactions with material and production workflows in manufacturing. Modeling these different aspects in one approach requires the use of hybrid
simulation (section 2.1.2). The individual simulation paradigms have their own
graphical approaches to represent models, “but they do not have obvious capabilities to depict the hybridization elements” [Br+2019, p. 727]. For this reason,
one focus of this thesis is placed on the conceptual model, which will be used in
© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH,
part of Springer Nature 2021
A. C. Römer, Simulation-based Optimization of Energy Efficiency in Production,
Forschung zur Digitalisierung der Wirtschaft | Advanced Studies
in Business Digitization, https://doi.org/10.1007/978-3-658-32971-6_2
9
Simulation-Based Optimization
There are things we can do to combat complexity. We
must use all the resources at our disposal to fight the
complexity dragon. At the moment, he’s winning.
JAMES O. Henriksen(Henriksen at the Titan’s Talk of
the Winter Simulation Conference 2006 [He2006, p. 2].)
“Simulation refers to a broad collection of methods and applications to mimic
the behavior of real systems, usually on a computer with appropriate software”
[KSZ2002, p. 3]. The use of simulation is a proven decision support tool in operational practice for production planning and control, whenever a complex system
of target figures, control parameters and disturbance variables are present, and the
number of system components results in a complex system behavior over time.
It is hardly feasible to handle this complexity by analytical methods and to give
tractable mathematical formulations [MW2011, p. 7]. Through the simulation, it
becomes possible to depict system-inherent causal relationships and to calculate
the result variables based on the dynamic runtime behavior. Simulation is a high
value tool but is not fully sufficient by itself. “An extra step is needed—a step that
joins simulation and optimization” [Ap+2004, p. 76]. This thesis uses modeling
and simulation techniques as a tool to depict the energy consumption as well as
its complex interactions with material and production workflows in manufacturing. Modeling these different aspects in one approach requires the use of hybrid
simulation (section 2.1.2). The individual simulation paradigms have their own
graphical approaches to represent models, “but they do not have obvious capabilities to depict the hybridization elements” [Br+2019, p. 727]. For this reason,
one focus of this thesis is placed on the conceptual model, which will be used in
© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH,
part of Springer Nature 2021
A. C. Römer, Simulation-based Optimization of Energy Efficiency in Production,
Forschung zur Digitalisierung der Wirtschaft | Advanced Studies
in Business Digitization, https://doi.org/10.1007/978-3-658-32971-6_2
9