4.1 Selection and Evaluation of Relevant Research Approaches
81
Lorenz, Hesse, and Fischer present a DES-based approach to simulate and
optimize energy consumption in complex automated production lines in the automotive industry using periodic time-expanded networks [LHF2012]. Assuming
the energy consumption behavior as being state-based, a consumption profile is
assigned to each process of a robot in the simulation. Every ending simulation
process as well as waiting processes create an entry into a data table, providing
the basis for energy consumption analysis for single robots as well as the whole
production line. To reduce peak-loads, a peak-load optimization process is added
to the simulation model, calculating the optimal starting time for all processes
whenever shifting execution periods is possible before starting the process. The
approach is validated in a car body shop showing that the introduction of predetermined waiting periods cause a peak-load drop by nearly 20% [LHF2012,
p. 2885].
Aiming at the development of an energy-oriented simulation-based approach
that enables every user in a producing company to independently develop and
assess simulation studies for the detection of potential energy efficiency improvements, Thiede presents a methodology consisting of ten process steps [Th2012].
Focusing on the general character of his concept, he specifies that the approach
should neither be limited to a specific case in production or a particular industrial sector, nor to a specific simulation software. In addition, all energy flows and
dependencies in production should be tracked. Thiede uses state-based energy
profiles to picture the consumption behavior of a machine depending on its state
of operation. To depict the interactions between the energy and the material
flows, he follows a discrete-continuous simulation approach. Thiede validates his
methodology in case studies from the automotive, the textile, and the electronic
industries. He proves the applicability of his approach regardless of the size of
production, the industry or the level of training of the executing employees and
shows that numerous optimization approaches can be developed and evaluated
based on his structured ten step plan. Through the use of the universally applicable
optimization library OptQuest™, Thiede includes optimization studies.
Heinzl et al. follow an interdisciplinary optimization approach for predicting
the impact of energy saving measures by comparing different production plant
scenarios [He+2013]. Combining the energy optimization of production processes
with separately analyzed aspects of the fields “machine and production system”,
“building” as well as the “energy system”, Heinzl et al. use the method of cosimulation to study the energetic interactions of the single subsystems. To cover
all energy aspects, several simulation models, which are executable as standalone modeling environments, are developed. Heinzl et al. use different simulation
81
Lorenz, Hesse, and Fischer present a DES-based approach to simulate and
optimize energy consumption in complex automated production lines in the automotive industry using periodic time-expanded networks [LHF2012]. Assuming
the energy consumption behavior as being state-based, a consumption profile is
assigned to each process of a robot in the simulation. Every ending simulation
process as well as waiting processes create an entry into a data table, providing
the basis for energy consumption analysis for single robots as well as the whole
production line. To reduce peak-loads, a peak-load optimization process is added
to the simulation model, calculating the optimal starting time for all processes
whenever shifting execution periods is possible before starting the process. The
approach is validated in a car body shop showing that the introduction of predetermined waiting periods cause a peak-load drop by nearly 20% [LHF2012,
p. 2885].
Aiming at the development of an energy-oriented simulation-based approach
that enables every user in a producing company to independently develop and
assess simulation studies for the detection of potential energy efficiency improvements, Thiede presents a methodology consisting of ten process steps [Th2012].
Focusing on the general character of his concept, he specifies that the approach
should neither be limited to a specific case in production or a particular industrial sector, nor to a specific simulation software. In addition, all energy flows and
dependencies in production should be tracked. Thiede uses state-based energy
profiles to picture the consumption behavior of a machine depending on its state
of operation. To depict the interactions between the energy and the material
flows, he follows a discrete-continuous simulation approach. Thiede validates his
methodology in case studies from the automotive, the textile, and the electronic
industries. He proves the applicability of his approach regardless of the size of
production, the industry or the level of training of the executing employees and
shows that numerous optimization approaches can be developed and evaluated
based on his structured ten step plan. Through the use of the universally applicable
optimization library OptQuest™, Thiede includes optimization studies.
Heinzl et al. follow an interdisciplinary optimization approach for predicting
the impact of energy saving measures by comparing different production plant
scenarios [He+2013]. Combining the energy optimization of production processes
with separately analyzed aspects of the fields “machine and production system”,
“building” as well as the “energy system”, Heinzl et al. use the method of cosimulation to study the energetic interactions of the single subsystems. To cover
all energy aspects, several simulation models, which are executable as standalone modeling environments, are developed. Heinzl et al. use different simulation