ZHANG Long, LIU Tao, LIU Min, WANG Xionghai. Scheduling Semiconductor Wafer Fabrication Using a New Fuzzy Association Classification Rules Based on Dynamic Fuzzy Partition[J]. Chinese Journal of Electronics, 2017, 26(1): 112-117. doi: 10.1049/cje.2016.11.006
Citation: ZHANG Long, LIU Tao, LIU Min, WANG Xionghai. Scheduling Semiconductor Wafer Fabrication Using a New Fuzzy Association Classification Rules Based on Dynamic Fuzzy Partition[J]. Chinese Journal of Electronics, 2017, 26(1): 112-117. doi: 10.1049/cje.2016.11.006

Scheduling Semiconductor Wafer Fabrication Using a New Fuzzy Association Classification Rules Based on Dynamic Fuzzy Partition

doi: 10.1049/cje.2016.11.006
Funds:  This work was supported by the National Key Basic Research and Development Program of China (2009CB320602), the National Natural Science Foundation of China (No.61025018, No.61021063, No.61104172), and the National Science and Technology Major Project of China (No.2011ZX02504-008).
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  • Corresponding author: LIU Tao (corresponding author) was born in 1980. He received the M.S. degree from Tsinghua University. At present, his research interests mainly include modelling, intelligent scheduling and optimization of complex manufacturing systems, etc. (Email:lt@tsinghua.edu.cn)
  • Received Date: 2014-11-14
  • Rev Recd Date: 2014-12-15
  • Publish Date: 2017-01-10
  • This paper proposes a dynamic fuzzy partition method of attribute domain suitable for the scheduling problem of Semiconductor wafer fabrication (SWF). Then, based on the above partition method, this paper gives a new Fuzzy association classification rules (FACRs) for scheduling SWF. Also, this paper presents a corresponding simple mining method used to obtain the effective FACRs based on the Apriori algorithm. Furthermore, a Harmony search (HS) algorithm is designed to determine the rule parameters including the minimum fuzzy support and the total number of linguistic values of each condition attribute for the simple fuzzy partition. At last, computational simulations and comparisons based on the practical data are provided. It is shown that the proposed FACRs can generate better results for almost all problem instances.
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