Stochastic Complex Networks as Predictive and Explanatory Model for the Dynamic Development of Production Logistic Systems

生产物流系统由制造系统的物理资源组成，但也包括原材料、产品、流程、订单、计划等，这是完成价值创造过程所必需的。特别是作业车间环境表现出复杂的结构和动态行为，因此对系统内变化的预测是复杂的。这些结构变化包括，例如，引入新机器的必要性、在车间放置机器、机器退役、安装新的运输路线、关闭过时的运输路线等。同时，对生产物流系统变化的可控性和可预测性的需求日益增加。这一方面是由于产品生命周期缩短和变体数量增加，另一方面是由于全球化带来的成本压力增加。如果企业不能及时调整制造业的结构，就会面临竞争劣势。本项目的目标是通过一个低成本的物料流随机模型，对制造系统的结构变化进行可靠的预测。基本的假设是，在物质流网络中存在主导模式，与其他模式相比，这些模式更有可能被观察到。该方法将车间内的物流建模为复杂网络，并在此基础上建立了随机块模型。该SBM用作制造系统的网络表示中的各种类型的变化的预测模型。来自制造商IT系统的真实的材料流数据作为模型创建的输入。预测的质量将与使用相同数据的最先进机器学习方法的预测结果进行比较。该项目的结果是制造系统领域一种新的、毫不费力的方法的概念和评估，用于控制动态和复杂的生产物流系统通过预测结构变化系统。该项目提供了一个机会，将该方法在随后的项目中的应用扩展到更广泛的领域，例如一般的物流过程。

Production logistic systems are composed of the physical resources of the manufacturing system, but also of raw material, products, processes, orders, plans, etc., which are required to complete the value creation process. Job shop environments in particular show complex structures and dynamic behavior, so that the anticipation of changes within the system is complicated. These structural changes include, e.g., the necessity to introduce new machines, the placement of machines on the shop floor, the decommissioning of machines, the installation of new transportation routes, the close-down of obsolete transportation routes, etc. At the same time, there is a rising need for controllability and predictability of changes in production logistic systems. This is caused by the development towards shorter product life cycles and a higher amount of variants on the one hand, in combination with increasing cost pressure due to the globalization on the other hand. If companies are not successful in the timely adaption of their structures in manufacturing, they will face competitive disadvantages.The goal of this project is to create reliable forecasts of structural changes in a manufacturing system with a stochastic model of the material flow with comparably low effort. The basic assumption is that there are predominant patterns in material flow networks, which are more probable to observe in comparison to other patterns. The approach is to model the material flow in a job shop as a complex network and to create a so called Stochastic Block Model (SBM) based on the network model. This SBM serves as a prediction model for various types of changes in a network representation of the manufacturing system. Real material flow data from the IT systems of manufacturers serve as input for the model creation. The quality of the prognosis will be compared to the prognosis results of state-of-the-art machine learning approaches using the same data.The result of the project is the concept and the evaluation of a new, effortless approach in the field of manufacturing systems for control of dynamic and complex production logistic systems by the prognosis of structural changes. The project offers the opportunity to extend the application of the approach in a subsequent project to a broader field, such as logistic processes in general.