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

对于制造企业来说，有针对性地有效利用可用资源以及经济优化变得越来越重要。根据精益哲学，制造系统的经济和组织优化主要与资源节约型制造的目标相一致。然而，这两个原则的兼容性是有限的。这就需要对有待改进的制造系统进行单独分析。此外，必须考虑潜在改进措施的利弊。该建议旨在开发一种基于仿真的方法，用于精益和资源高效制造系统的多目标优化，并将理想的典型制造系统的相互依赖性推广为精益和资源高效制造系统的指导方针。为了预测理想典型制造系统的各种配置的效果，离散事件仿真的材料、能量和信息流将与来自精益和资源高效制造的目标标准的评估模型相耦合。使用实验设计，高斯过程模型将适合于复杂仿真模型的行为。这些元模型允许在短时间内计算系统行为的近似值，因此，将能够对制造系统的影响和相互依赖性进行广泛的多目标优化和统计分析。由于计算时间长，这在模拟模型中几乎是不可能的。精益和资源节约型制造系统的分析效果和相互依赖性将转化为每个理想的典型制造系统的指导方针。随后，将通过选定的工业案例研究对方法和制定的准则进行测试，并可能加以调整。最后，分析的工业制造系统的最佳目标状态将被确定使用其元模型。在此基础上，将确定真实的实践措施，选择最合适的措施组合，并确定其最佳实施顺序。

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.