Abstract
Due-date assignment (DDA) is the first important task of shop floor control in wafer fabrication. Due-date related performance is impacted by the quality of the DDA rules. Assigning order due dates and timely delivering the goods to the customer will enhance customer service and competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN) prediction is considered in this work. An ANN-based DDA rule combined with simulation technology and statistical analysis is developed. Besides, regression-based DDA rules for wafer fabrication are modelled as benchmarking. Whether neural networks can outperform conventional and regression-based DDA rules taken from the literature is examined.
From the simulation and statistical results, ANN-based DDA rules perform a better job in due-date prediction. ANN-based DDA rules have a lower tardiness rate than the other rules. ANN-based DDA rules have better sensitivity and variance than the other rules. Therefore, if the wafer fab information is not difficult to obtain, the ANN-based DDA rule can perform better due-date prediction. The SFM_sep and JIQ in regression-based and conventional rules are better than the others.
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Abbreviations
- DDA:
-
due-date assignment
- ANN:
-
artificial neural network
- BPN:
-
back-propagation network
- SFC:
-
shop floor control
- AI:
-
artificial intelligence
- TWK:
-
due-date prediction rule based on total amount of works
- SLK:
-
due-date prediction rule based on slack time
- NOP:
-
due-date prediction rule based on number of operations
- JIQ:
-
due-date prediction rule based on current queue length in system
- JIBQ:
-
due-date prediction rule based on queue length in bottleneck station
- WIP:
-
work in process
- PSP:
-
pre-shop-pool
- KFM:
-
regression-based due-date prediction rule considering key factor
- SFM:
-
regression-based due-date prediction rule considering significant factors
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Acknowledgement
This research acknowledges the subvention from National Science Council (NSC) project: NSC 91-2213-E-009-113
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Sha, D.Y., Hsu, S.Y. Due-date assignment in wafer fabrication using artificial neural networks. Int J Adv Manuf Technol 23, 768–775 (2004). https://doi.org/10.1007/s00170-003-1644-8
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DOI: https://doi.org/10.1007/s00170-003-1644-8