Using an artificial neural network prediction model to optimize work-in-process inventory level for wafer fabrication

Authors:
Yu-Hsin Lin;Jie-Ren Shie;Chih-Hung Tsai
Affiliations:
Department of Industrial Engineering and Management, Ming-Hsin University of Science and Technology, No. 1, Hsin-Hsin Road, Hsin-Feng, 30401 Hsin-Chu County, Taiwan;Department of Mechanical Engineering, Ming-Hsin University of Science and Technology, No. 1, Hsin-Hsin Road, Hsin-Feng, 30401 Hsin-Chu County, Taiwan;Department of Industrial Engineering and Management, Ta-Hwa Institute of Technology, 1 Ta-Hwa Road, Chung-Lin, Hsin-Chu, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 7
Cited 3

Practical methods of optimization; (2nd ed.)

Practical methods of optimization; (2nd ed.)
Simulation of a semiconductor manufacturing line

Communications of the ACM - Special issue on simulation
Bayesian interpolation

Neural Computation
Neural networks applications in manufacturing processes

Proceedings of the 14th annual conference on Computers and industrial engineering
Neural-expert hybrid approach for intelligent manufacturing: a survey

Computers in Industry
The study of applying ANP model to assess dispatching rules for wafer fabrication

Expert Systems with Applications: An International Journal
A scheduling rule for job release in semiconductor fabrication

Operations Research Letters

Heuristic method on solving an inventory model for products with optional components under stochastic payment and budget constraints

Expert Systems with Applications: An International Journal
A prediction interval-based approach to determine optimal structures of neural network metamodels

Expert Systems with Applications: An International Journal
A comparative analysis of power demand forecasting with artificial intelligence and traditional approach

International Journal of Business Information Systems

Quantified Score

Hi-index	12.05

Visualization

Abstract

A proper selection of a work-in-process (WIP) inventory level has great impact onto the productivity of wafer fabrication processes, which can be properly used to trigger the decision of when to release specific wafer lots. However, the selection of an optimal WIP is always a tradeoff amongst the throughput rate, the cycle time and the standard deviation of the cycle time. This study focused on finding an optimal WIP value of wafer fabrication processes by developing an algorithm integrating an artificial neural network (ANN) and the sequential quadratic programming (SQP) method. With this approach, it offered an effective and systematic way to identify an optimal WIP level. Hence, the efficiency of finding the optimal WIP level was greatly improved.