80
4 State of the Art
Summarizing the research approaches of intersection II, two main approaches
can be distinguished. Most publications follow a DES-based model including
the energy use of machines either as state-based variables or the energy use is
included as a state-based consumption with cumulative load profiles. Only a few
publications describe a hybrid simulation approach to realistically model the system of highly dynamic production processes. Regardless of the chosen approach,
the simulation model generation is described as highly complex and time consuming. Often support software is required in order to manage, aggregate, and
evaluate the collected energy data for the simulation models. The system boundaries of the considered production areas are therefore chosen very diversely among
the approaches, ranging from the consideration of consumers that are only directly
involved in the production process to the consideration of support processes and
peripheral equipment.
4.1.4 Studies on Simulation-Based optimization of Energy
Efficiency in Production
Studies on the simulation-based optimization of energy efficiency in production
converge the three disciplines of OR, OM, and M&S and therefore require a holistic view on producing companies, an understanding of all processes, the relevant
process in- and outputs, as well as their existing dynamic interactions. Aiming at
the integration of energy efficiency goals in the decision system of a production,
an approach combining simulation and optimization studies should be usable as a
decision supporting tool in a complex and dynamic industrial environment. Fulfilling the above-mentioned limitations for the paper selection process, only the
subsequently listed seven research papers can be identified as relevant.
Rager developed a concept for an energy-oriented utilization of identical
parallel machines with the objectives of minimizing the number of occupied
machines to smooth the use of energy throughout the production time [Ra2008].
By applying heuristic methods for solving the optimization problem based on
hybrid evolutionary algorithms, Rager develops a decision model to support the
energy-oriented machine scheduling process. He proves the applicability of his
approach in a case study of the textile industry. The discrete event simulation
software eM-Plant is used for visualization purposes. Depending on the number of production orders, the simulation model execution shows, that energy cost
savings between 10 and 20% and load peak reductions up to 30% can be achieved
[Ra2008, pp. 120–121].
4 State of the Art
Summarizing the research approaches of intersection II, two main approaches
can be distinguished. Most publications follow a DES-based model including
the energy use of machines either as state-based variables or the energy use is
included as a state-based consumption with cumulative load profiles. Only a few
publications describe a hybrid simulation approach to realistically model the system of highly dynamic production processes. Regardless of the chosen approach,
the simulation model generation is described as highly complex and time consuming. Often support software is required in order to manage, aggregate, and
evaluate the collected energy data for the simulation models. The system boundaries of the considered production areas are therefore chosen very diversely among
the approaches, ranging from the consideration of consumers that are only directly
involved in the production process to the consideration of support processes and
peripheral equipment.
4.1.4 Studies on Simulation-Based optimization of Energy
Efficiency in Production
Studies on the simulation-based optimization of energy efficiency in production
converge the three disciplines of OR, OM, and M&S and therefore require a holistic view on producing companies, an understanding of all processes, the relevant
process in- and outputs, as well as their existing dynamic interactions. Aiming at
the integration of energy efficiency goals in the decision system of a production,
an approach combining simulation and optimization studies should be usable as a
decision supporting tool in a complex and dynamic industrial environment. Fulfilling the above-mentioned limitations for the paper selection process, only the
subsequently listed seven research papers can be identified as relevant.
Rager developed a concept for an energy-oriented utilization of identical
parallel machines with the objectives of minimizing the number of occupied
machines to smooth the use of energy throughout the production time [Ra2008].
By applying heuristic methods for solving the optimization problem based on
hybrid evolutionary algorithms, Rager develops a decision model to support the
energy-oriented machine scheduling process. He proves the applicability of his
approach in a case study of the textile industry. The discrete event simulation
software eM-Plant is used for visualization purposes. Depending on the number of production orders, the simulation model execution shows, that energy cost
savings between 10 and 20% and load peak reductions up to 30% can be achieved
[Ra2008, pp. 120–121].