Material flow management refers to the analysis and specific optimization of the inventory‐production system. Material flow can be characterized as the organized flow of material in a production process with the required sequence determined by a technological procedure. The Milk run system assures the transportation of materials at the right time and in an optimal manner. It should be combined with the Kanban system to highlight when something is required in the production process. This paper presents biological swarm intelligence, in general, and a particular model, particle swarm optimization (PSO), for modelling material flow using a Milk run system supported by a Kanban system in the automotive industry. The aim of this study is to create a new model for the optimal number of trailers for one train and optimal number of containers in a tugger train system when the route time period has been defined. A new modified PSO approach for integrating inventory‐production in a unique optimization model is used. The major modification to the original PSO is using the capacity of a container instead of a velocity component. Each new Kanban trigger is checked, and the total timing for the Milk run delivery solution is calculated for the necessary raw material capacity for each shop floor.

中文翻译：

---

在汽车行业中使用Milk run和看板系统对物料流进行建模

物料流管理是指对库存生产系统的分析和特定的优化。物料流可以表征为生产过程中物料的有组织流，其中所需的顺序由工艺程序确定。所述牛奶运行系统保证输送物料在正确的时间和以最佳的方式。应该将其与看板系统结合使用，以突出显示生产过程中何时需要某些内容。本文总体上介绍了生物群智能，以及使用牛奶运行系统对物料流进行建模的特定模型粒子群优化（PSO）。在汽车行业中由看板系统支持。这项研究的目的是创建一种新模型，用于在定义路线时间段后，针对一列火车的最佳挂车数量和牵引式火车系统中的最佳集装箱数量。使用了一种新的经过修改的PSO方法，用于将库存生产集成到唯一的优化模型中。对原始PSO的主要修改是使用容器的容量而不是速度分量。检查每个新的看板触发器，并针对每个车间所需的原料容量计算出牛奶运行交付解决方案的总时间。