Dynamic dispatching system using a deep denoising autoencoder for semiconductor manufacturing

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. Rece...

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Veröffentlicht in:Applied soft computing Jg. 86; S. 105904
Hauptverfasser: Lee, Sangmin, Kim, Hae Joong, Kim, Seoung Bum
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.01.2020
Schlagworte:
Class imbalance problem
Deep denoising autoencoder
Dispatching rule selection
Novelty detection
Storage allocation
Dispatching rule selection
Storage allocation
Deep denoising autoencoder
Novelty detection
Class imbalance problem
ISSN:1568-4946, 1872-9681
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Zusammenfassung: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. •We propose a dynamic dispatching system for storage allocation.•We propose to use a deep denoising autoencoder to address class imbalance problem.•Proposed method is robust against production uncertainties.•A real-time simulation presents the utility and superiority of the proposed method.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2019.105904