A multiple criteria decision for trading capacity between two semiconductor fabs

Muh-Cherng Wu*, Wen Jen Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This paper presents a multiple criteria decision approach for trading weekly tool capacity between two semiconductor fabs. Due to the high-cost characteristics of tools, a semiconductor company with multiple fabs (factories) may weekly trade their tool capacities. That is, a lowly utilized workstation in one fab may sell capacity to its highly utilized counterpart in the other fab. Wu and Chang [Wu, M. C., & Chang, W. J. (2007). A short-term capacity trading method for semiconductor fabs with partnership. Expert Systems with Application, 33(2), 476-483] have proposed a method for making weekly trading decisions between two wafer fabs. Compared with no trading, their method could effectively increase the two fabs' throughput for a longer period such as 8 weeks. However, their trading decision-making is based on a single criterion-number of weekly produced operations, which may still leave a space for improving. We therefore proposed a multiple criteria trading decision approach in order to further increase the two fabs' throughput. The three decision criteria are: number of operations, number of layers, and number of wafers. This research developed a method to find an optimal weighting vector for the three criteria. The method firstly used NN + GA (neural network + genetic algorithm) to find an optimal trading decision in each week, and then used DOE + RSM (design of experiment + response surface method) to find an optimal weighting vector for a longer period, say 10 weeks. Experiments indicated that the multiple criteria approach indeed outperformed the previous method in terms the fabs' long-term throughput.

Original languageEnglish
Pages (from-to)938-945
Number of pages8
JournalExpert Systems with Applications
Issue number3
StatePublished - 1 Oct 2008


  • Capacity trading
  • Design of experiment
  • Genetic algorithm
  • Neural network
  • Response surface method
  • Semiconductor


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