Dynamic dispatching system using a deep denoising autoencoder for semiconductor manufacturing

Sangmin Lee, Hae Joong Kim, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Deep denoising autoencoders (DDAE), which are variants of the autoencoder, have shown outstanding performance in various machine learning tasks. In this study, we propose using a DDAE to address a dispatching rule selection problem that represents a major problem in semiconductor manufacturing. Recently, the significance of dispatching systems for storage allocation has become more apparent because operational issues lead to transfer inefficiency, resulting in production losses. Further, recent approaches have overlooked the possibility of a class imbalance problem in predicting the best dispatching rule. The main purpose of this study is to examine DDAE-based predictive control of the storage dispatching systems to reduce idle machines and production losses. We conducted an experimental evaluation to compare the predictive performance of DDAE with those of five other novelty detection algorithms. Finally, we compared our adaptive approach with the optimization and existing heuristic approaches to demonstrate the effectiveness and efficiency of the proposed method. The experimental results demonstrated that the proposed method outperformed the existing methods in terms of machine utilizations and throughputs.

Original languageEnglish
Article number105904
JournalApplied Soft Computing Journal
Publication statusPublished - 2020 Jan


  • Class imbalance problem
  • Deep denoising autoencoder
  • Dispatching rule selection
  • Novelty detection
  • Storage allocation

ASJC Scopus subject areas

  • Software


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