Abstract
The integration of simulation-based optimization and data mining is an emerging approach to support decision-making in the design and improvement of manufacturing systems. In such an approach, knowledge extracted from the optimal solutions generated by the simulation-based optimization process can provide important information to decision makers, such as the importance of the decision variables and their influence on the design objectives, which cannot easily be obtained by other means. However, can the extracted knowledge be directly used during the optimization process to further enhance the quality of the solutions? This paper proposes such an online knowledge extraction approach that is used together with a preference-guided multi-objective optimization algorithm on simulation models of manufacturing systems. Specifically, it introduces a combination of the multi-objective evolutionary optimization algorithm, NSGA-II, and a customized data mining algorithm, called Flexible Pattern Mining (FPM), which can extract knowledge in the form of rules in an online and automatic manner, in order to guide the optimization to converge towards a decision maker’s preferred region in the objective space. Through a set of application problems, this paper demonstrates how the proposed FPM-NSGA-II can be used to support higher quality decision-making in manufacturing.
Highlights
It is widely acknowledged that manufacturing is the engine of the modern economy, due to its leading contribution to overall productivity and its multiple effects on growth in the rest of the economy [1]
We present the extension of Flexible Pattern Mining (FPM)-based decision support system to facilitate online knowledge extraction, i.e. during the optimization run
The evaluations and the FPM rules from each generation of all optimization runs are stored in the database, which can be retrieved and plotted either during or after the optimization run
Summary
It is widely acknowledged that manufacturing is the engine of the modern economy, due to its leading contribution to overall productivity and its multiple effects on growth in the rest of the economy [1]. Knowing what actions to perform to maintain and improve efficiency can be hard, due to the complex nature of manufacturing processes. Researchers have shown a significant interest in merging optimization and simulation in order to find the optimal or near-optimal design and/or operating policies for manufacturing systems [5]. It has huge potential to generate optimal or near-optimal solutions
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