E-resources
Peer reviewed
Open access
-
Sun, H.
International journal of simulation modelling, 09/2023, Volume: 22, Issue: 3Journal Article
In response to Industry 4.0 and the rise of intelligent manufacturing, this study develops a system combining Long Short-Term Memory (LSTM) Neural Networks and a Multi-Objective Genetic Algorithm to improve prediction and optimization in manufacturing scheduling. A novel model predicts work-in-process (WIP) inventory using LSTM neural networks, accommodating dynamic changes in production. A manufacturing scheduling model is also created and solved using a multi-objective genetic algorithm, simplifying the resolution process and obtaining practical solutions. These methods provide a valuable approach to optimizing production scheduling in intelligent manufacturing, enhancing efficiency and economic gains.
Author
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.