Model Simplification Methods for Coded Discrete-Event Simulation Models : A Systematic Review and Experimental Study

In an increasingly competitive market due to customer demands of customization and an increasing rate of new product variant introductions, companies need to explore new tools to support them to better predict and optimally re-configure their production networks. In terms of the factory flow level, discrete-event simulation and simulation-based optimization represent such kinds of tools available at the disposal of production engineers or managers. For a complex factory consisting of multiple production lines, creating detailed simulation models of these lines and connecting them together can be used for optimization, but the computational complexity can be prohibitively large for acquiring results in time. Model simplification methods can be utilized to reduce the computational complexity of a model. In this study, a systematic literature review is conducted with the aim of identifying simplification methods for coded models, characteristics of the detailed model, type of industry, motivation, and validation measures. Based on the results of the literature review an experimental study wherein the limits of a specific simplification method are analyzed. We compare the output of a dynamically created model with the output of a simplified representation. A correlation can be observed between outputs for medium to large lines, but for smaller lines, there is a larger discrepancy. The simplification method allows for the reduction in simulation runtime, enabling simulation-based optimization of large lines or interconnected simplified models forming a production network, i.e., a factory, to be optimized and analyzed more efficiently, leading to competitive advantages for companies.Preprint submitted to international journal, August 25, 2021.