Reacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage levels, reducing lead time, and lowering batch sizes, showing the potential of optimizing on the factory level. Furthermore, a base for decision-support is presented, generating clusters from the optimization results. These clusters are then used as targets for a decision tree algorithm, creating rules for reaching different solutions for a decision-maker to choose from. Thereby allowing decisions to be driven by data, and not by intuition.

通过改善和适应工业系统，快速响应不断变化的市场需求和新的变体是一项重要的业务优势。系统变更成本高昂；尤其是当这些系统已经到位时。应该将投入的资源作为目标，以便最大程度地改善结果。允许这种情况的一种方法是将离散事件模拟，聚合模型，多目标优化和数据挖掘结合在一起，如本文所示。进行了针对一个工业问题的实际优化案例研究，从而降低了存储水平，缩短了交货时间并减小了批量大小，显示了在工厂级别进行优化的潜力。此外，提供了决策支持的基础，可根据优化结果生成聚类。然后，将这些聚类用作决策树算法的目标，创建规则以达到决策者可以选择的不同解决方案。从而允许决策由数据而非直觉驱动。