68
4 State of the Art
This chapter 1 gives an overview of the selection and evaluation process of
relevant research approaches in section 4.1, followed by a discussion of results
in section 4.2 as well as the derivation of the research demand in section 4.3.
Reference to the analyzed publications and advanced literature will be made in
the appropriate places of the text.
4.1
Selection and Evaluation of Relevant Research
Approaches
The simulation-based optimization of energy efficiency combines concepts from
Operations Management, such as the integration of energy efficiency goals in production, Operations Research, e.g., the use of integrated optimization algorithms
as well as Modeling and Simulation, describing all techniques for model implementation and execution 2 . By using a Venn diagram, Figure 4.1 illustrates the
convergence of the involved disciplines. Being embodied in the areas of OM,
the integration of energy efficiency goals in the normative, strategic, and operative production management is mandatory for following a holistic and sustainable
consistent approach regarding material and energy use throughout all phases of a
production [DS2008, p. 97].
While the problem formulation phase of including energy aspects in simulation
approaches can be seen in the discipline of OM, the remaining steps of guiding
a model builder through a simulation study are based in the discipline of M&S.
The success of implementing and executing a simulation model directly depends
on how extensively the steps of model conceptualization, data collection, model
translation, experimental design, the production runs and analysis and furthermore
the reporting and documentation have been accomplished 3 [Ba+2005, pp. 12–16;
Wa2009, pp. 27–28]. M&S techniques such as the discrete event simulation, continuous system simulation (CSS), other specific classes to simulate dynamic system
behavior (e.g., DTSS, DESS, and DEVS) or a combination of different theories leading to a hybrid simulation approach, are typically used [ZPK2010, p. 8].
Located in the discipline of OR, optimization algorithms are required to come
1 Extracts of this chapter have already been published at the Winter Simulation Conference
2016 [RS2016].
2 Yilmaz and Ören offer a comprehensive and integrative overview of M&S from different
perspectives for further reading [Ör2009, pp. 3–33].
3 A detailed description of the single simulation study steps can be found in respective literature
[Ba+2005, p. 12–16; La2007, p. 66–70].
4 State of the Art
This chapter 1 gives an overview of the selection and evaluation process of
relevant research approaches in section 4.1, followed by a discussion of results
in section 4.2 as well as the derivation of the research demand in section 4.3.
Reference to the analyzed publications and advanced literature will be made in
the appropriate places of the text.
4.1
Selection and Evaluation of Relevant Research
Approaches
The simulation-based optimization of energy efficiency combines concepts from
Operations Management, such as the integration of energy efficiency goals in production, Operations Research, e.g., the use of integrated optimization algorithms
as well as Modeling and Simulation, describing all techniques for model implementation and execution 2 . By using a Venn diagram, Figure 4.1 illustrates the
convergence of the involved disciplines. Being embodied in the areas of OM,
the integration of energy efficiency goals in the normative, strategic, and operative production management is mandatory for following a holistic and sustainable
consistent approach regarding material and energy use throughout all phases of a
production [DS2008, p. 97].
While the problem formulation phase of including energy aspects in simulation
approaches can be seen in the discipline of OM, the remaining steps of guiding
a model builder through a simulation study are based in the discipline of M&S.
The success of implementing and executing a simulation model directly depends
on how extensively the steps of model conceptualization, data collection, model
translation, experimental design, the production runs and analysis and furthermore
the reporting and documentation have been accomplished 3 [Ba+2005, pp. 12–16;
Wa2009, pp. 27–28]. M&S techniques such as the discrete event simulation, continuous system simulation (CSS), other specific classes to simulate dynamic system
behavior (e.g., DTSS, DESS, and DEVS) or a combination of different theories leading to a hybrid simulation approach, are typically used [ZPK2010, p. 8].
Located in the discipline of OR, optimization algorithms are required to come
1 Extracts of this chapter have already been published at the Winter Simulation Conference
2016 [RS2016].
2 Yilmaz and Ören offer a comprehensive and integrative overview of M&S from different
perspectives for further reading [Ör2009, pp. 3–33].
3 A detailed description of the single simulation study steps can be found in respective literature
[Ba+2005, p. 12–16; La2007, p. 66–70].