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...
Uložené v:
| Vydané v: | Applied soft computing Ročník 86; s. 105904 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.01.2020
|
| Predmet: | |
| ISSN: | 1568-4946, 1872-9681 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | 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. |
|---|---|
| AbstractList | 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. |
| ArticleNumber | 105904 |
| Author | Lee, Sangmin Kim, Hae Joong Kim, Seoung Bum |
| Author_xml | – sequence: 1 givenname: Sangmin orcidid: 0000-0002-5215-2546 surname: Lee fullname: Lee, Sangmin email: smlee5679@gmail.com organization: Department of Industrial Management Engineering, Korea University, 145 Anam-Ro, Seoungbuk-Gu, Anam-dong, Seoul 02841, South Korea – sequence: 2 givenname: Hae Joong surname: Kim fullname: Kim, Hae Joong email: haejoong.kim@gmail.com organization: Material Handling Automation Group, Samsung Electronics, SamsungJeonJa-ro 1, Hwaseong-si, Gyeonggi-do, 18448, South Korea – sequence: 3 givenname: Seoung Bum orcidid: 0000-0002-2205-8516 surname: Kim fullname: Kim, Seoung Bum email: sbkim1@korea.ac.kr organization: Department of Industrial Management Engineering, Korea University, 145 Anam-Ro, Seoungbuk-Gu, Anam-dong, Seoul 02841, South Korea |
| BookMark | eNp9kMtKAzEUQIMo2FZ_wNX8wNQ8mswE3Eh9QsGNLiVkkjua0iYlyQj9ezOMKxfd3Bf3XLhnjs598IDQDcFLgom43S51CmZJMZFlwCVenaEZaRtaS9GS81Jz0dYruRKXaJ7SFhdI0naGPh-OXu-dqaxLB53Nt_NfVTqmDPtqSGOjKwtwKMEHNw2GHMCbYCFWfYhVgsIHbweTS7fXfui1yUMsu1foote7BNd_eYE-nh7f1y_15u35dX2_qQ3DONdUakallZI3xHKMueg0NKLhrO0kSC76jmPDKTAKgtGGSEsNx4wB7wA3DVugdrprYkgpQq-Myzq74HPUbqcIVqMmtVWjJjVqUpOmgtJ_6CG6vY7H09DdBEF56sdBVMm44gSsi2CyssGdwn8BczCEqQ |
| CitedBy_id | crossref_primary_10_1016_j_eswa_2024_124354 crossref_primary_10_1007_s10696_022_09449_8 crossref_primary_10_3390_pr9030407 crossref_primary_10_1115_1_4068667 crossref_primary_10_1007_s11571_020_09642_1 crossref_primary_10_1080_00207543_2022_2148011 crossref_primary_10_1109_JSEN_2024_3506154 crossref_primary_10_1016_j_eswa_2021_116206 crossref_primary_10_1109_TSM_2020_2965293 crossref_primary_10_3390_sym12091458 crossref_primary_10_1016_j_asoc_2022_109122 crossref_primary_10_3390_app13084808 crossref_primary_10_1109_ACCESS_2021_3051763 |
| Cites_doi | 10.1109/ISSM.2006.4493023 10.1016/j.sigpro.2003.07.018 10.1109/ICCIT.2007.237 10.1016/j.cie.2014.02.002 10.1287/opre.9.3.383 10.1109/ICDMW.2010.19 10.1109/TR.2012.2220898 10.1016/j.engappai.2016.12.012 10.1016/j.engappai.2016.02.015 10.1016/j.engappai.2015.07.013 10.1007/s00170-005-0190-y 10.1016/j.asoc.2015.09.027 10.1080/00207543.2016.1178406 10.1023/A:1007660820062 10.1145/1390156.1390294 10.1007/BF00118083 10.1162/089976601750264965 10.21437/Interspeech.2011-91 10.1016/j.asoc.2007.11.005 10.1016/j.ejor.2006.06.018 10.1145/1007730.1007733 10.1109/TSM.2004.831925 10.1080/0020754032000123579 10.1023/A:1022627411411 10.1016/j.omega.2015.01.012 10.1145/130385.130401 10.1145/342009.335388 10.1214/aos/1176348376 10.1080/00207540600675819 10.1016/j.asoc.2018.01.010 10.1007/11893257_3 |
| ContentType | Journal Article |
| Copyright | 2019 Elsevier B.V. |
| Copyright_xml | – notice: 2019 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.asoc.2019.105904 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-9681 |
| ExternalDocumentID | 10_1016_j_asoc_2019_105904 S1568494619306854 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SST SSV SSZ T5K UHS UNMZH ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c300t-29a329d99571d50056bae767538b9e956fb50c52e32e632719d2c5033e5be0773 |
| ISICitedReferencesCount | 18 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000503388200035&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1568-4946 |
| IngestDate | Sat Nov 29 07:01:51 EST 2025 Tue Nov 18 21:24:00 EST 2025 Fri Feb 23 02:49:32 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Dispatching rule selection Storage allocation Deep denoising autoencoder Novelty detection Class imbalance problem |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-29a329d99571d50056bae767538b9e956fb50c52e32e632719d2c5033e5be0773 |
| ORCID | 0000-0002-2205-8516 0000-0002-5215-2546 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_asoc_2019_105904 crossref_primary_10_1016_j_asoc_2019_105904 elsevier_sciencedirect_doi_10_1016_j_asoc_2019_105904 |
| PublicationCentury | 2000 |
| PublicationDate | January 2020 2020-01-00 |
| PublicationDateYYYYMMDD | 2020-01-01 |
| PublicationDate_xml | – month: 01 year: 2020 text: January 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied soft computing |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Xu, Tse (b37) 2018 Chawla, Japkowicz, Drive (b19) 2004; 6 Lee, Cho (b22) 2006; 4233 P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, ICML, 2010, pp. 3371–3408. Wakabayashi, Watanabe, Kobayashi, Okabe, Koike (b1) 2004; 17 M.M. Breunig, H.-P. Kriegel, R.T. Ng, J. Sander, LOF: Identifying density-based local outliers, in: Proc. 2000 Acm Sigmod Int. Conf. Manag. Data, 2000, pp. 1–12. Bishop (b23) 2006 Zhao, Yan, Chen, Mao, Wang, Gao (b36) 2016 Kim, Oh, Shin, Jung, Chae, Lee (b44) 2007; 45 Bartlett, Lee, Ahmed, Nemhauser, Sokol, Na (b42) 2014; 70 El-Bouri, Shah (b14) 2006; 31 Bengio, Lamblin, Popovici, Larochelle (b30) 2007; 19 Heger, Branke, Hildebrandt, Scholz-Reiter (b16) 2016; 54 Little (b3) 1961; 9 Erhan, Courville, Vincent (b38) 2010; 11 S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning 2015, June, pp. 448–456. M.C. Chen, R.J. Wang, A.P. Chen, An empirical study for the detection of corporate financial anomaly using outlier mining techniques, in: 2007 Int. Conf. Converg. Inf. Technol., ICCIT 2007, 2007, pp. 612–617. V. Nair, G.E. Hinton, Rectified linear units improve restricted Boltzmann machines, in: Proc. 27th Int. Conf. Mach. Learn., 2010. P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proc. 25th Int. Conf. Mach. Learn. - ICML ’08, 2008, pp. 1096–1103. Chiu (b24) 1991; 19 Xiong, Wang, Liu, Zhou, Hou, Liu (b34) 2016; 52 Siebert, Bartlett, Kim, Ahmed, Lee, Nazzal, Nemhauser, Sokol (b43) 2018 Choe, Kim, Ryu (b15) 2016 Heger, Bani, Scholz-Reiter (b17) 2012; 43 Cortes, Vapnik (b28) 1995; 20 B. Scholz-Reiter, J. Heger, T. Hildebrandt, Gaussian processes for dispatching rule selection scheduling: Comparison of learning techniques, in: Proc. - IEEE Int. Conf. Data Mining, ICDM, 2010. Liu, Dong (b12) 1996; 7 Ali, Shamsuddin, Ralescu (b20) 2015; 7 Grozdić, Jovičić, Subotić (b33) 2017; 59 Kingma, Ba (b41) 2015; 2015 Chen, Ji (b5) 2007; 181 Schölkopf, Platt, Shawe-Taylor, Smola, Williamson (b29) 2001; 13 C.M. Liu, C.J. Kuo, H.Y. Chi, A dynamic method for optimal WIP allocation and control in a semiconductor manufacturing system, in: IEEE Int. Symp. Semicond. Manuf. Conf. Proc., 2006, pp. 61–65. De Armas, Lalla-Ruiz, Expósito-Izquierdo, Landa-Silva, Melián-Batista (b7) 2015; 45 Abdelmaguid, Nassef, Kamal, Hassan (b13) 2004 D. Yu, M.L. Seltzer, Improved bottleneck features using pretrained deep neural networks, in: Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, 2011. Lee (b9) 2008 Markou, Singh (b25) 2003; 83 Lin, Chang (b8) 2013; 62 Jerin Leno, Saravanasankar, Ponnambalam (b4) 2012 García-Álvarez, González, Vela (b11) 2018 Japkowicz (b21) 2001; 42 Rowshannahad, Dauzere-Peres, Cassini (b2) 2015; 54 B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Proc. Fifth Annu. Work. Comput. Learn. Theory - COLT ’92, 1992, pp. 144–152. 10.1016/j.asoc.2019.105904_b10 García-Álvarez (10.1016/j.asoc.2019.105904_b11) 2018 10.1016/j.asoc.2019.105904_b32 Schölkopf (10.1016/j.asoc.2019.105904_b29) 2001; 13 10.1016/j.asoc.2019.105904_b31 Grozdić (10.1016/j.asoc.2019.105904_b33) 2017; 59 10.1016/j.asoc.2019.105904_b35 Ali (10.1016/j.asoc.2019.105904_b20) 2015; 7 Liu (10.1016/j.asoc.2019.105904_b12) 1996; 7 Cortes (10.1016/j.asoc.2019.105904_b28) 1995; 20 Erhan (10.1016/j.asoc.2019.105904_b38) 2010; 11 Little (10.1016/j.asoc.2019.105904_b3) 1961; 9 10.1016/j.asoc.2019.105904_b6 El-Bouri (10.1016/j.asoc.2019.105904_b14) 2006; 31 Lee (10.1016/j.asoc.2019.105904_b9) 2008 Choe (10.1016/j.asoc.2019.105904_b15) 2016 Chen (10.1016/j.asoc.2019.105904_b5) 2007; 181 De Armas (10.1016/j.asoc.2019.105904_b7) 2015; 45 Lin (10.1016/j.asoc.2019.105904_b8) 2013; 62 Jerin Leno (10.1016/j.asoc.2019.105904_b4) 2012 10.1016/j.asoc.2019.105904_b18 10.1016/j.asoc.2019.105904_b39 Chawla (10.1016/j.asoc.2019.105904_b19) 2004; 6 Bengio (10.1016/j.asoc.2019.105904_b30) 2007; 19 Chiu (10.1016/j.asoc.2019.105904_b24) 1991; 19 Kim (10.1016/j.asoc.2019.105904_b44) 2007; 45 Heger (10.1016/j.asoc.2019.105904_b16) 2016; 54 Siebert (10.1016/j.asoc.2019.105904_b43) 2018 10.1016/j.asoc.2019.105904_b27 10.1016/j.asoc.2019.105904_b26 Wakabayashi (10.1016/j.asoc.2019.105904_b1) 2004; 17 Xiong (10.1016/j.asoc.2019.105904_b34) 2016; 52 Zhao (10.1016/j.asoc.2019.105904_b36) 2016 10.1016/j.asoc.2019.105904_b40 Bartlett (10.1016/j.asoc.2019.105904_b42) 2014; 70 Heger (10.1016/j.asoc.2019.105904_b17) 2012; 43 Markou (10.1016/j.asoc.2019.105904_b25) 2003; 83 Bishop (10.1016/j.asoc.2019.105904_b23) 2006 Xu (10.1016/j.asoc.2019.105904_b37) 2018 Abdelmaguid (10.1016/j.asoc.2019.105904_b13) 2004 Lee (10.1016/j.asoc.2019.105904_b22) 2006; 4233 Kingma (10.1016/j.asoc.2019.105904_b41) 2015; 2015 Japkowicz (10.1016/j.asoc.2019.105904_b21) 2001; 42 Rowshannahad (10.1016/j.asoc.2019.105904_b2) 2015; 54 |
| References_xml | – reference: C.M. Liu, C.J. Kuo, H.Y. Chi, A dynamic method for optimal WIP allocation and control in a semiconductor manufacturing system, in: IEEE Int. Symp. Semicond. Manuf. Conf. Proc., 2006, pp. 61–65. – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: b28 article-title: Support-vector networks publication-title: Mach. Learn. – volume: 52 start-page: 194 year: 2016 end-page: 202 ident: b34 article-title: ECG signal enhancement based on improved denoising auto-encoder publication-title: Eng. Appl. Artif. Intell. – year: 2006 ident: b23 article-title: Pattern Recognition and Machine Learning – volume: 19 start-page: 1883 year: 1991 end-page: 1905 ident: b24 article-title: Bandwidth selection for kernel density estimation publication-title: Ann. Statist. – volume: 7 start-page: 243 year: 1996 end-page: 250 ident: b12 article-title: Dispatching rule selection using artificial neural networks for dynamic planning and scheduling publication-title: J. Intell. Manuf. – volume: 83 start-page: 2481 year: 2003 end-page: 2497 ident: b25 article-title: Novelty detection: A review - Part 1: Statistical approaches publication-title: Signal Process. – reference: M.M. Breunig, H.-P. Kriegel, R.T. Ng, J. Sander, LOF: Identifying density-based local outliers, in: Proc. 2000 Acm Sigmod Int. Conf. Manag. Data, 2000, pp. 1–12. – volume: 6 start-page: 1 year: 2004 end-page: 6 ident: b19 article-title: Editorial: Special issue on learning from imbalanced data sets publication-title: ACM SIGKDD Explor. Newsl. – reference: V. Nair, G.E. Hinton, Rectified linear units improve restricted Boltzmann machines, in: Proc. 27th Int. Conf. Mach. Learn., 2010. – reference: P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, ICML, 2010, pp. 3371–3408. – year: 2016 ident: b15 article-title: Online preference learning for adaptive dispatching of AGVs in an automated container terminal publication-title: Appl. Soft Comput. J. – volume: 70 start-page: 176 year: 2014 end-page: 182 ident: b42 article-title: Congestion-aware dynamic routing in automated material handling systems publication-title: Comput. Ind. Eng. – reference: D. Yu, M.L. Seltzer, Improved bottleneck features using pretrained deep neural networks, in: Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, 2011. – volume: 62 start-page: 92 year: 2013 end-page: 104 ident: b8 article-title: A novel reliability evaluation technique for stochastic-flow manufacturing networks with multiple production lines publication-title: IEEE Trans. Reliab. – year: 2004 ident: b13 article-title: A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles publication-title: Int. J. Prod. Res. – volume: 45 start-page: 350 year: 2015 end-page: 360 ident: b7 article-title: A hybrid GRASP-VNS for ship routing and scheduling problem with discretized time windows publication-title: Eng. Appl. Artif. Intell. – year: 2018 ident: b11 article-title: Metaheuristics for solving a real-world electric vehicle charging scheduling problem publication-title: Appl. Soft Comput. J. – volume: 17 start-page: 317 year: 2004 end-page: 323 ident: b1 article-title: High-speed AMHS and its operation method for 300-mm QTAT fab publication-title: IEEE Trans. Semicond. Manuf. – volume: 54 start-page: 50 year: 2015 end-page: 59 ident: b2 article-title: Capacitated qualification management in semiconductor manufacturing publication-title: Omega – volume: 11 start-page: 625 year: 2010 end-page: 660 ident: b38 article-title: Why does unsupervised pre-training help deep learning? publication-title: J. Mach. Learn. Res. – volume: 181 start-page: 515 year: 2007 end-page: 522 ident: b5 article-title: A mixed integer programming model for advanced planning and scheduling (APS) publication-title: European J. Oper. Res. – volume: 54 start-page: 6812 year: 2016 end-page: 6824 ident: b16 article-title: Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times publication-title: Int. J. Prod. Res. – volume: 43 year: 2012 ident: b17 article-title: Improving production scheduling with machine learning publication-title: Artif. Intell. Logist. – volume: 42 start-page: 97 year: 2001 end-page: 122 ident: b21 article-title: Supervised versus unsupervised binary-learning by feedforward neural networks publication-title: Mach. Learn. – volume: 59 start-page: 15 year: 2017 end-page: 22 ident: b33 article-title: Whispered speech recognition using deep denoising autoencoder publication-title: Eng. Appl. Artif. Intell. – volume: 31 start-page: 342 year: 2006 end-page: 349 ident: b14 article-title: A neural network for dispatching rule selection in a job shop publication-title: Int. J. Adv. Manuf. Technol. – volume: 2015 start-page: 1 year: 2015 end-page: 15 ident: b41 article-title: Adam: a method for stochastic optimization publication-title: Int. Conf. Learn. Represent. – reference: B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Proc. Fifth Annu. Work. Comput. Learn. Theory - COLT ’92, 1992, pp. 144–152. – year: 2018 ident: b37 article-title: Automatic roller bearings fault diagnosis using DSAE in deep learning and CFS algorithm publication-title: Soft Comput. – volume: 7 start-page: 176 year: 2015 end-page: 204 ident: b20 article-title: Classification with class imbalance problem: A review publication-title: Int. J. Adv. Soft Comput. Its Appl. – volume: 45 start-page: 789 year: 2007 end-page: 802 ident: b44 article-title: Effectiveness of vehicle reassignment in a large-scale overhead hoist transport system publication-title: Int. J. Prod. Res. – volume: 19 start-page: 153 year: 2007 ident: b30 article-title: Greedy layer-wise training of deep networks publication-title: Adv. Neural Inf. Process. Syst. – reference: P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proc. 25th Int. Conf. Mach. Learn. - ICML ’08, 2008, pp. 1096–1103. – year: 2016 ident: b36 article-title: Deep learning and its applications to machine health monitoring publication-title: Mech. Syst. Signal Process. – start-page: 1 year: 2018 end-page: 13 ident: b43 article-title: Decision policies for semiconductor manufacturing: optimisation under uncertainty with simulation validation publication-title: Int. J. Prod. Res. – year: 2008 ident: b9 article-title: Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment publication-title: Appl. Soft Comput. J. – volume: 9 start-page: 383 year: 1961 end-page: 387 ident: b3 article-title: A proof for the queuing formula: L = publication-title: Oper. Res. – year: 2012 ident: b4 article-title: Layout design for efficient material flow path publication-title: Procedia Eng. – volume: 13 start-page: 1443 year: 2001 end-page: 1471 ident: b29 article-title: Estimating the support of a high-dimensional distribution publication-title: Neural Comput. – reference: B. Scholz-Reiter, J. Heger, T. Hildebrandt, Gaussian processes for dispatching rule selection scheduling: Comparison of learning techniques, in: Proc. - IEEE Int. Conf. Data Mining, ICDM, 2010. – reference: S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning 2015, June, pp. 448–456. – volume: 4233 start-page: 21 year: 2006 end-page: 30 ident: b22 article-title: The novelty detection approach for difference degrees of class imbalance publication-title: Lecture Notes in Comput. Sci. – reference: M.C. Chen, R.J. Wang, A.P. Chen, An empirical study for the detection of corporate financial anomaly using outlier mining techniques, in: 2007 Int. Conf. Converg. Inf. Technol., ICCIT 2007, 2007, pp. 612–617. – ident: 10.1016/j.asoc.2019.105904_b6 doi: 10.1109/ISSM.2006.4493023 – volume: 19 start-page: 153 year: 2007 ident: 10.1016/j.asoc.2019.105904_b30 article-title: Greedy layer-wise training of deep networks publication-title: Adv. Neural Inf. Process. Syst. – start-page: 1 year: 2018 ident: 10.1016/j.asoc.2019.105904_b43 article-title: Decision policies for semiconductor manufacturing: optimisation under uncertainty with simulation validation publication-title: Int. J. Prod. Res. – volume: 83 start-page: 2481 issue: 12 year: 2003 ident: 10.1016/j.asoc.2019.105904_b25 article-title: Novelty detection: A review - Part 1: Statistical approaches publication-title: Signal Process. doi: 10.1016/j.sigpro.2003.07.018 – ident: 10.1016/j.asoc.2019.105904_b35 doi: 10.1109/ICCIT.2007.237 – volume: 70 start-page: 176 year: 2014 ident: 10.1016/j.asoc.2019.105904_b42 article-title: Congestion-aware dynamic routing in automated material handling systems publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2014.02.002 – year: 2018 ident: 10.1016/j.asoc.2019.105904_b37 article-title: Automatic roller bearings fault diagnosis using DSAE in deep learning and CFS algorithm publication-title: Soft Comput. – volume: 9 start-page: 383 year: 1961 ident: 10.1016/j.asoc.2019.105904_b3 article-title: A proof for the queuing formula: L = λ w publication-title: Oper. Res. doi: 10.1287/opre.9.3.383 – ident: 10.1016/j.asoc.2019.105904_b10 doi: 10.1109/ICDMW.2010.19 – volume: 11 start-page: 625 year: 2010 ident: 10.1016/j.asoc.2019.105904_b38 article-title: Why does unsupervised pre-training help deep learning? publication-title: J. Mach. Learn. Res. – volume: 62 start-page: 92 issue: 1 year: 2013 ident: 10.1016/j.asoc.2019.105904_b8 article-title: A novel reliability evaluation technique for stochastic-flow manufacturing networks with multiple production lines publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2012.2220898 – volume: 59 start-page: 15 year: 2017 ident: 10.1016/j.asoc.2019.105904_b33 article-title: Whispered speech recognition using deep denoising autoencoder publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2016.12.012 – volume: 52 start-page: 194 year: 2016 ident: 10.1016/j.asoc.2019.105904_b34 article-title: ECG signal enhancement based on improved denoising auto-encoder publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2016.02.015 – ident: 10.1016/j.asoc.2019.105904_b39 – volume: 45 start-page: 350 year: 2015 ident: 10.1016/j.asoc.2019.105904_b7 article-title: A hybrid GRASP-VNS for ship routing and scheduling problem with discretized time windows publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2015.07.013 – volume: 31 start-page: 342 year: 2006 ident: 10.1016/j.asoc.2019.105904_b14 article-title: A neural network for dispatching rule selection in a job shop publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-005-0190-y – ident: 10.1016/j.asoc.2019.105904_b32 – year: 2016 ident: 10.1016/j.asoc.2019.105904_b15 article-title: Online preference learning for adaptive dispatching of AGVs in an automated container terminal publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2015.09.027 – volume: 54 start-page: 6812 year: 2016 ident: 10.1016/j.asoc.2019.105904_b16 article-title: Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times publication-title: Int. J. Prod. Res. doi: 10.1080/00207543.2016.1178406 – volume: 42 start-page: 97 year: 2001 ident: 10.1016/j.asoc.2019.105904_b21 article-title: Supervised versus unsupervised binary-learning by feedforward neural networks publication-title: Mach. Learn. doi: 10.1023/A:1007660820062 – ident: 10.1016/j.asoc.2019.105904_b31 doi: 10.1145/1390156.1390294 – volume: 7 start-page: 243 issue: 3 year: 1996 ident: 10.1016/j.asoc.2019.105904_b12 article-title: Dispatching rule selection using artificial neural networks for dynamic planning and scheduling publication-title: J. Intell. Manuf. doi: 10.1007/BF00118083 – volume: 7 start-page: 176 year: 2015 ident: 10.1016/j.asoc.2019.105904_b20 article-title: Classification with class imbalance problem: A review publication-title: Int. J. Adv. Soft Comput. Its Appl. – volume: 13 start-page: 1443 year: 2001 ident: 10.1016/j.asoc.2019.105904_b29 article-title: Estimating the support of a high-dimensional distribution publication-title: Neural Comput. doi: 10.1162/089976601750264965 – ident: 10.1016/j.asoc.2019.105904_b40 – year: 2006 ident: 10.1016/j.asoc.2019.105904_b23 – ident: 10.1016/j.asoc.2019.105904_b18 doi: 10.21437/Interspeech.2011-91 – year: 2012 ident: 10.1016/j.asoc.2019.105904_b4 article-title: Layout design for efficient material flow path publication-title: Procedia Eng. – year: 2008 ident: 10.1016/j.asoc.2019.105904_b9 article-title: Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2007.11.005 – volume: 181 start-page: 515 year: 2007 ident: 10.1016/j.asoc.2019.105904_b5 article-title: A mixed integer programming model for advanced planning and scheduling (APS) publication-title: European J. Oper. Res. doi: 10.1016/j.ejor.2006.06.018 – volume: 6 start-page: 1 year: 2004 ident: 10.1016/j.asoc.2019.105904_b19 article-title: Editorial: Special issue on learning from imbalanced data sets publication-title: ACM SIGKDD Explor. Newsl. doi: 10.1145/1007730.1007733 – volume: 17 start-page: 317 issue: 3 year: 2004 ident: 10.1016/j.asoc.2019.105904_b1 article-title: High-speed AMHS and its operation method for 300-mm QTAT fab publication-title: IEEE Trans. Semicond. Manuf. doi: 10.1109/TSM.2004.831925 – year: 2004 ident: 10.1016/j.asoc.2019.105904_b13 article-title: A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles publication-title: Int. J. Prod. Res. doi: 10.1080/0020754032000123579 – volume: 20 start-page: 273 year: 1995 ident: 10.1016/j.asoc.2019.105904_b28 article-title: Support-vector networks publication-title: Mach. Learn. doi: 10.1023/A:1022627411411 – volume: 54 start-page: 50 year: 2015 ident: 10.1016/j.asoc.2019.105904_b2 article-title: Capacitated qualification management in semiconductor manufacturing publication-title: Omega doi: 10.1016/j.omega.2015.01.012 – ident: 10.1016/j.asoc.2019.105904_b27 doi: 10.1145/130385.130401 – ident: 10.1016/j.asoc.2019.105904_b26 doi: 10.1145/342009.335388 – year: 2016 ident: 10.1016/j.asoc.2019.105904_b36 article-title: Deep learning and its applications to machine health monitoring publication-title: Mech. Syst. Signal Process. – volume: 19 start-page: 1883 year: 1991 ident: 10.1016/j.asoc.2019.105904_b24 article-title: Bandwidth selection for kernel density estimation publication-title: Ann. Statist. doi: 10.1214/aos/1176348376 – volume: 45 start-page: 789 year: 2007 ident: 10.1016/j.asoc.2019.105904_b44 article-title: Effectiveness of vehicle reassignment in a large-scale overhead hoist transport system publication-title: Int. J. Prod. Res. doi: 10.1080/00207540600675819 – volume: 43 year: 2012 ident: 10.1016/j.asoc.2019.105904_b17 article-title: Improving production scheduling with machine learning publication-title: Artif. Intell. Logist. – year: 2018 ident: 10.1016/j.asoc.2019.105904_b11 article-title: Metaheuristics for solving a real-world electric vehicle charging scheduling problem publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2018.01.010 – volume: 4233 start-page: 21 year: 2006 ident: 10.1016/j.asoc.2019.105904_b22 article-title: The novelty detection approach for difference degrees of class imbalance publication-title: Lecture Notes in Comput. Sci. doi: 10.1007/11893257_3 – volume: 2015 start-page: 1 year: 2015 ident: 10.1016/j.asoc.2019.105904_b41 article-title: Adam: a method for stochastic optimization publication-title: Int. Conf. Learn. Represent. |
| SSID | ssj0016928 |
| Score | 2.3801286 |
| Snippet | Deep denoising autoencoders (DDAE), which are variants of the autoencoder, have shown outstanding performance in various machine learning tasks. In this study,... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 105904 |
| SubjectTerms | Class imbalance problem Deep denoising autoencoder Dispatching rule selection Novelty detection Storage allocation |
| Title | Dynamic dispatching system using a deep denoising autoencoder for semiconductor manufacturing |
| URI | https://dx.doi.org/10.1016/j.asoc.2019.105904 |
| Volume | 86 |
| WOSCitedRecordID | wos000503388200035&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect (Freedom Collection) customDbUrl: eissn: 1872-9681 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016928 issn: 1568-4946 databaseCode: AIEXJ dateStart: 20010601 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1NT9wwELVa6KGXln6ptAX5wG0VlDjxJj7yVQFCCAla7aWKnNipFoGzYpOKn8-M7WSz0KJy4GLtWo432nn7djKeeUPIVhVLXsZaBuAq8yDRIfCglEnAOHjnaamUsnXcP0_S09NsMhFnPt12btsJpMZkt7di9qymhjkwNpbOPsHc_aYwAa_B6DCC2WH8L8Pvux7zePQyA561ESan1zxqbWBAjpTWMxhMPXUTbVOjniXKSmDW4RwT5muDSrDw7lqaFssfbD3j0JftHNg5MLlNTW-bbsUiw-dcmt_X08FRv8XfocSCtHqx2s-faySf0a7Xh_DRCBYOohGeQMdZkAgfVvQMmw0pEh0613H4AXu7QMLltgRgYtad2F4sXpbKvvcX1icWdjlrlznukeMeudvjJVllKRdAfKs7RweT4_6oaSxsA97-xn1llUsCvH8nf_deBh7JxRp54x8l6I6DwDvyQpv35G3XpoN61v5AfnlE0AEiqEMEtYigkiIiaI8IOkAEBUTQJUTQJUR8JD--H1zsHQa-q0ZQxmHYBEzImAklBE8jxVEKtpAaJX3irBAaHpergoclZzpmehyzNBKKlXjYrXkBP-U0_kRWTG30Z0JDKRIdVayKsiwpxkpgiXyVVCyTWpQqXCdR923lpZecx84nV_m_7bRORv01Mye48uhq3hkh9y6jcwVzwNQj13150qd8Ja8XWP9GVpqbVm-QV-WfZjq_2fSAugOC4I7e |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Dynamic+dispatching+system+using+a+deep+denoising+autoencoder+for+semiconductor+manufacturing&rft.jtitle=Applied+soft+computing&rft.au=Lee%2C+Sangmin&rft.au=Kim%2C+Hae+Joong&rft.au=Kim%2C+Seoung+Bum&rft.date=2020-01-01&rft.issn=1568-4946&rft.volume=86&rft.spage=105904&rft_id=info:doi/10.1016%2Fj.asoc.2019.105904&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_asoc_2019_105904 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1568-4946&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1568-4946&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1568-4946&client=summon |