Methodology for multiple response optimization of lean and resource efficient manufacturing systems

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For manufacturing companies, the targeted and efficient use of available resources is gaining in importance, alongside economic optimization. The economic and organizational optimization of manufacturing systems according to the lean philosophy is mainly compatible with the goals of resource-efficient manufacturing. However, there are limits to the compatibility of the two principles. This necessitates individual analyses of to-be improved manufacturing systems. Furthermore, advantages and disadvantages of potential improvement measures must be considered. This proposal aims at developing a simulation based methodology for the multi-objective optimization of lean and resource-efficient manufacturing systems and for the generalization of ideal typical manufacturing systems' interdependencies into guidelines for lean and resource efficient manufacturing systems. To predict the effects of various configurations of ideal typical manufacturing systems, the material, energy, and information flows of a discrete event simulation will be coupled with an assessment model from the target criteria of lean and resource-efficient manufacturing. Using design of experiments, Gaussian process models will be fitted to the behavior of the complex simulation models. These meta-models allow the approximation of the system behavior to be computed in a short period of time and, thus, extensive multi-objective optimization and statistical analysis of a manufacturing system's effects and interdependencies will be enabled. This would hardly be possible with the simulation model, due to long computational time. The analyzed effects and interdependencies of lean and resource-efficient manufacturing systems will be translated into guidelines for each ideal typical manufacturing system. Subsequently, the methodology and the developed guidelines will be tested and possibly adapted by selected industrial case studies. Finally, the optimal target states of the analyzed industrial manufacturing systems will be identified using their meta-models. Based on this, real practice measures will be determined, the most suitable combination of measures will be selected, and their optimal implementation sequence will be determined.