An MDP model-based reinforcement learning approach for production station ramp-up optimization: Q-learning analysis
- Submitting institution
-
Loughborough University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 309
- Type
- D - Journal article
- DOI
-
10.1109/TSMC.2013.2294155
- Title of journal
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Article number
- -
- First page
- 1125
- Volume
- 44
- Issue
- 9
- ISSN
- 2168-2216
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work has impacted the state of the art by reducing by up to a factor of 2 the time taken to move a production line from commissioning to full scale production by training the system offline prior to installation. This research led to further work on ramp up process through EU project openMOS with end-users including Ford, Electrolux, Senseair and system integrators Introsys, technology provider Weplus. Manufacturing institute HSSMI part sponsored a PhD project based on this research.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -