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

随机复杂网络作为生产物流系统动态发展的预测和解释模型

基本信息

批准号：
310784388
负责人：
Professor Dr. Till Becker
金额：
--
依托单位：
Fachbereich Wirtschaft
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/310784388?language=en
关键词：
Stochastic Complex Networks Predictive Explanatory

项目摘要

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.

生产物流系统由制造系统的物理资源组成，还包括完成价值创造过程所需的原材料、产品、流程、订单、计划等。作业车间环境尤其表现出复杂的结构和动态行为，因此系统内变化的预期变得复杂。这些结构性变化包括，例如，引入新机器的必要性、机器在车间的放置、机器的退役、安装新的运输路线、关闭过时的运输路线等。，对生产物流系统变化的可控性和可预测性的需求不断增长。一方面是由于产品生命周期越来越短、品种越来越多，另一方面全球化带来的成本压力越来越大。如果公司不能成功地及时调整其制造结构，他们将面临竞争劣势。该项目的目标是通过物料流的随机模型以相对较低的工作量创建制造系统结构变化的可靠预测。基本假设是物质流网络中存在主要模式，与其他模式相比，这些模式更有可能被观察到。该方法是将作业车间中的物料流建模为复杂网络，并基于该网络模型创建所谓的随机块模型（SBM）。该 SBM 用作制造系统网络表示中各种类型变化的预测模型。来自制造商 IT 系统的真实物料流数据可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的机器学习方法的预测结果进行比较。该项目的结果是制造领域一种新的、轻松的方法的概念和评估通过预测结构变化来控制动态和复杂的生产物流系统的系统。该项目提供了将该方法在后续项目中的应用扩展到更广泛领域的机会，例如一般的物流流程。