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Due-date assignment in wafer fabrication using artificial neural networks

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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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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Chiao Tung University, Hsin-Chu, Taiwan R.O.C.

    D. Y. Sha & S. Y. Hsu

Authors
  1. D. Y. Sha
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  2. S. Y. Hsu
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Correspondence to D. Y. Sha.

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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

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