Fast algorithm for predicting the production process performance in flexible production lines with delayed differentiation

In flexible manufacturing lines with delayed differentiation, the production process may fluctuate sharply when a control action is performed. As a result, the steady-state analysis algorithm is inaccurate for these production lines, and transient behavior studies have become crucial. However, dynam...

Full description

Saved in:
Bibliographic Details
Published in:IIE transactions 2024-09, Vol.ahead-of-print (ahead-of-print), p.1-13
Main Authors: Chen, Jingchuan, Shen, Zuo-Jun Max
Format: Article
Language:English
Subjects:
Algorithms
Assembly lines
Differentiation
Flexible manufacturing
Flexible manufacturing systems
operation control
Performance prediction
prediction algorithm
Production controls
production line
Production lines
Steady state
Stochastic models
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In flexible manufacturing lines with delayed differentiation, the production process may fluctuate sharply when a control action is performed. As a result, the steady-state analysis algorithm is inaccurate for these production lines, and transient behavior studies have become crucial. However, dynamic analysis remains unexplored compared with the well-established theoretical system of steady-state analysis. Therefore, in this study, we propose a fast algorithm for predicting the production process performance in the delayed differentiation-based flexible production line under operation control. We first formulate practical problems existing in the auto, food, and furniture industries into a mathematical formation. Then, we offer closed-form formulae for predicting the production process performance using the built stochastic model in the production line with three machines. We also propose an algorithm to predict the performance of a production line having more than three machines. The proposed methods were verified to be highly accurate through comparison experiments. In terms of theoretical contributions, this study offers a research foundation for other transient-based studies. From a practical perspective, the proposed algorithms can be employed to predict the production process performance of processing lines under production control in advance.
ISSN:2472-5854
2472-5862
DOI:10.1080/24725854.2022.2126564