Journal
APPLIED SOFT COMPUTING
Volume 86, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2019.105904
Keywords
Deep denoising autoencoder; Novelty detection; Dispatching rule selection; Storage allocation; Class imbalance problem
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Funding
- Brain Korea PLUS
- Ministry of Trade, Industry & Energy under Industrial Technology Innovation Program [R1623371]
- Institute for Information &Communications Technology Promotion grant - Korea government [2018-0-00440]
- Ministry of Culture, Sports and Tourism
- Korea Creative Content Agency in the Culture Technology Research & Development Program 2019
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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. (C) 2019 Elsevier B.V. All rights reserved.
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