Knowledge Discovery in Production Simulation By Interleaving Multi-Objective Optimization and Data Mining

This paper introduces a novel methodology for the optimization, analysis and decision support in production systems development. The methodology is based on the innovization procedure, originally introduced for unveiling new and innovative design principles in engineering design problems. The innovization (innovation via optimization) procedure stretches beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. By integrating the concept of innovization with simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies on the decision rules extracted from the multi-objective optimization (MOO) using data mining (DM) are used to modify the original optimization so that faster convergence to the desired solution of the decision maker can be achieved. In other words, faster convergence and deeper knowledge of the relationships between the key decision variables and objectives can be obtained by interleaving the MOO and DM processes. In this paper, such an interleaved approach is illustrated through a set of experiments carried out to a simulation model developed in a real-world production system improvement project.