Reduced Discrete-Event Simulation Models for Medium-Term Production Scheduling

Model simplification in manufacturing simulation – Review and framework

“…For example, starting from a detailed manufacturing model, Johnson et al (2005) propose a simple procedure that (i) establishes a list of machines ordered according to their utilization, (ii) selects those machines with a low utilization from the list and replaces them with constant delays, and (iii) validates the resulting reduced model by comparing its outputs with those found for the detailed model. Similar examples, employing different methods and different manufacturing settings, are given by Innis and Rexstad (1983), Yin and Zhou (1989), Salt (1993), Law (1991), Nance et al (1999), Chwif, Barretto, andSantoro (1998), Chwif et al, 2000, Yavari andRoeder (2012), Rank et al (2016) • Misfit between model nature and modeling objectives • Henriksen (1989) • Problem size • Morris (1967), Salt (1993), Chwif et al (2000) • Number of model inputs • Innis and Rexstad (1983), Kim et al (2003), Yavari and Roeder (2012) Brooks and Tobias (2000), Völker and Gmilkowsky (2003), Huber and Dangelmaier (2009), and Zhou, Cao, Liu, and Zhang (2016). Apart from their choices of underlying methods and focus on specific manufacturing settings, simplification procedures differ for their algorithmic and/or tool-based support.…”

Architecture for simulation-based performance assessment of planning approaches in semiconductor manufacturing

“…For these reasons, a reduced simulation model focusing on the principal characteristics of the base system and process is used in our architecture. In the following, Algorithm 2 describes the methodology proposed by Hung and Leachman (1999) and Völker and Gmilkowsky (2003) for reducing the degree of detail of simulation models. The methodology is applied to a variant of the MASM test data set MIMAC-I (MASM 1997).…”

Controlled simplification of material flow simulation models

“…Most implementations use the methods aggregation and formalism transformation. The latter is better known as simulation metamodelling and shall not be discussed further in this paper, since it is not possible to create a stepwise simplification using these techniques (Völker and Gmilkowsky 2003). Aggregation is used by Brooks and Tobias (2000), Johnson et al (2005), Rose (2000), Rose (2007) and Hung and Leachman (1999).…”