136
5 Development of a Simulation-based Methodology …
Depending on the state of the machine, the ‘prepare machine function’ calls
either the ‘prepare from off function’ when the drilling machine has not been
switched on already, it calls the ‘prepare from standby function’ for the case
that the drilling machine is in standby state, or it calls the ‘prepare from idle
function’ unless the current state is the idle state to calculate the estimated time
for the drilling machine to be ready to act (Figure 5.34). By calling the various
functions, it is ensured that the machines of the production line are ready on time
but are not switched on and consuming energy before the planned use. The exact
timing of the machines holds high savings potential, especially for machines that
are needed late in the production process.
The above described functionalities are added to the simulation model in preparation to define the most efficient production line setup in the optimization
experiments.
In addition to the optimization potentials mentioned above, there are further
approaches on the way to energy-efficient production. Following, two are briefly
discussed below but not dealt with in more detail. If several products and product
variants are produced on a production line, an energy-optimized order scheduling
can be used to generate a further setting lever for optimizations. Since the different products manufactured on the fictional line all produce very similar energy
consumption profiles, an energy-optimal order scheduling is not relevant for this
case study. The same applies to potential savings that can be realized by planning
energy-intensive production processes during the night shift in order to be able to
use the more favorable electricity conditions from 10 pm to 6 am. The author is
aware of the fact, that these potentials for optimization exist and may be used in
practice, but they are not covered in this book.
5.6.4 Depiction of the Optimization Potential
Following the optimization scenarios in section 5.4.1, two main objective functions for the production line are defined. Firstly, the total energy consumption is
minimized with a clear focus on the elimination of non-value adding production
times. Secondly, occurring power consumption peak values should be minimized.
To find the minimum total consumption value E total for the total energy consumed by the production line in a four-week cycle, a new optimization experiment
in AnyLogic is defined.
The objective function is defined in the optimization experiment settings. As
it is not possible, to include a stock element from the SD library at this point, a
5 Development of a Simulation-based Methodology …
Depending on the state of the machine, the ‘prepare machine function’ calls
either the ‘prepare from off function’ when the drilling machine has not been
switched on already, it calls the ‘prepare from standby function’ for the case
that the drilling machine is in standby state, or it calls the ‘prepare from idle
function’ unless the current state is the idle state to calculate the estimated time
for the drilling machine to be ready to act (Figure 5.34). By calling the various
functions, it is ensured that the machines of the production line are ready on time
but are not switched on and consuming energy before the planned use. The exact
timing of the machines holds high savings potential, especially for machines that
are needed late in the production process.
The above described functionalities are added to the simulation model in preparation to define the most efficient production line setup in the optimization
experiments.
In addition to the optimization potentials mentioned above, there are further
approaches on the way to energy-efficient production. Following, two are briefly
discussed below but not dealt with in more detail. If several products and product
variants are produced on a production line, an energy-optimized order scheduling
can be used to generate a further setting lever for optimizations. Since the different products manufactured on the fictional line all produce very similar energy
consumption profiles, an energy-optimal order scheduling is not relevant for this
case study. The same applies to potential savings that can be realized by planning
energy-intensive production processes during the night shift in order to be able to
use the more favorable electricity conditions from 10 pm to 6 am. The author is
aware of the fact, that these potentials for optimization exist and may be used in
practice, but they are not covered in this book.
5.6.4 Depiction of the Optimization Potential
Following the optimization scenarios in section 5.4.1, two main objective functions for the production line are defined. Firstly, the total energy consumption is
minimized with a clear focus on the elimination of non-value adding production
times. Secondly, occurring power consumption peak values should be minimized.
To find the minimum total consumption value E total for the total energy consumed by the production line in a four-week cycle, a new optimization experiment
in AnyLogic is defined.
The objective function is defined in the optimization experiment settings. As
it is not possible, to include a stock element from the SD library at this point, a