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Discrete event simulation is an accepted technology for investigating the dynamic behavior of complex manufacturing systems. Visualizations created within simulation studies often focus on the animation of the dynamic processes of a single simulation run, supplemented with graphs of certain performance indicators obtained from replications of a simulation run or a few manually conducted simulation...
Data mining tools have been around for several decades, but the term “big data” has only recently captured widespread attention. Numerous success stories have been promulgated as organizations have sifted through massive volumes of data to find interesting patterns that are, in turn, transformed into actionable information. Yet a key drawback to the big data paradigm is that it relies on observational...
This paper describes a decision support system (DSS) built on knowledge extraction using simulation-based optimization and data mining. The paper starts with a requirements analysis based on a survey conducted with a number of industrial companies about their practices of using simulations for decision support. Based upon the analysis, a new, interactive DSS that can fulfill the industrial requirements,...
Discrete-event simulation is a well-accepted method for planning, evaluating, and monitoring processes in production and logistics contexts. In order to reduce time and effort spent on creating the simulation model, automatic simulation model generation is an important area in modeling methodology research. When automatically generating a simulation model from existing data sources, the correct reproduction...
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