File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1080/24725854.2022.2126564
- Scopus: eid_2-s2.0-85141580683
- WOS: WOS:000880221700001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Fast algorithm for predicting the production process performance in flexible production lines with delayed differentiation
Title | Fast algorithm for predicting the production process performance in flexible production lines with delayed differentiation |
---|---|
Authors | |
Keywords | Flexible manufacturing operation control performance prediction prediction algorithm production line |
Issue Date | 8-Nov-2022 |
Publisher | Taylor and Francis Group |
Citation | IISE Transactions, 2022 How to Cite? |
Abstract | 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. |
Persistent Identifier | http://hdl.handle.net/10722/331295 |
ISSN | 2023 Impact Factor: 2.0 2023 SCImago Journal Rankings: 0.862 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Jingchuan | - |
dc.contributor.author | Shen, Zuo-Jun Max | - |
dc.date.accessioned | 2023-09-21T06:54:27Z | - |
dc.date.available | 2023-09-21T06:54:27Z | - |
dc.date.issued | 2022-11-08 | - |
dc.identifier.citation | IISE Transactions, 2022 | - |
dc.identifier.issn | 2472-5854 | - |
dc.identifier.uri | http://hdl.handle.net/10722/331295 | - |
dc.description.abstract | <p>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.</p> | - |
dc.language | eng | - |
dc.publisher | Taylor and Francis Group | - |
dc.relation.ispartof | IISE Transactions | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Flexible manufacturing | - |
dc.subject | operation control | - |
dc.subject | performance prediction | - |
dc.subject | prediction algorithm | - |
dc.subject | production line | - |
dc.title | Fast algorithm for predicting the production process performance in flexible production lines with delayed differentiation | - |
dc.type | Article | - |
dc.identifier.doi | 10.1080/24725854.2022.2126564 | - |
dc.identifier.scopus | eid_2-s2.0-85141580683 | - |
dc.identifier.eissn | 2472-5862 | - |
dc.identifier.isi | WOS:000880221700001 | - |
dc.identifier.issnl | 2472-5854 | - |