Toward data-driven and multi-scale modeling for material flow simulation: Comparison of modeling methods

被引：0
|
作者：
Nagahara, Satoshi [1 ,2 ]
Kaihara, Toshiya [2 ]
Fujii, Nobutada [2 ]
Kokuryo, Daisuke [2 ]
机构：
[1] Hitachi Ltd, Ind Automat Res Dept, Yokoham, Kanagawa 2440817, Japan
[2] Kobe Univ, Grad Sch Syst Informat, Kobe, Hyogo 6578501, Japan
来源：
IFAC PAPERSONLINE | 2023年 / 56卷 / 02期
关键词：
Material flow simulation; queueing system; machine learning;
D O I：
10.1016/j.ifacol.2023.10.1146
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Material flow simulation is a powerful tool to realize efficient operation in complicated production systems such as high-mix and low-volume production. However, it takes significant efforts and expertise to construct accurate simulation models. We have proposed a semi-automatic modeling approach called as data-driven and multi-scale modeling in which various modeling methods are combined to maximize the simulation accuracy for entire production system. In this article, we introduce the overview of the proposed method and experimental results on simple production systems with multiple machines. Copyright (c) 2023 The Authors.
引用
收藏
页码：7834 / 7839
页数：6
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