{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T09:14:19Z","timestamp":1766049259420,"version":"3.41.2"},"reference-count":43,"publisher":"ASME International","issue":"3","license":[{"start":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T00:00:00Z","timestamp":1579219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 51475334"],"award-info":[{"award-number":["Grant No. 51475334"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Reentrant flow plays an important role for the allocation of limited resources in semiconductor manufacturing. In particular, over- or under-loading of workstations may deteriorate performances of the whole production line. Therefore, load balancing is usually accomplished with dispatching rules to balance the workload to enhance production performance. Focus on the realistic needs, a novel prediction-based dynamic scheduling method with a multi-layer perceptron (MLP) is proposed for load balancing. This study proposed MLP based on the simulation dataset of empirical industrial fabrication facilities as the prediction model. The prediction outputs incorporated into the dynamic dispatching rule (DDR) for optimal load balancing based on the queue length at each workstation, named as a dynamic scheduling method considering load balancing (DSMLB). Based on the validation, DSMLB compared with the state-of-the-art dispatching rules shows that DSMLB has improved the daily movement, equipment utilization (EU), throughput rate, and cycle time (CT).<\/jats:p>","DOI":"10.1115\/1.4045742","type":"journal-article","created":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T16:52:55Z","timestamp":1576601575000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":4,"title":["A Predictive Dispatching Rule Assisted by Multi-Layer Perceptron for Scheduling Wafer Fabrication Lines"],"prefix":"10.1115","volume":"20","author":[{"given":"Qingyun","family":"Yu","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haolin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuo-Yi","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;"},{"name":"Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;"},{"name":"Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2020,1,17]]},"reference":[{"issue":"8","key":"2021022709544732800_CIT0001","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1109\/TSMC.2017.2669212","article-title":"An Operation-Group Based Soft Scheduling Approach for Uncertain Semiconductor Wafer Fabrication System","volume":"48","author":"Zhong","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern.-Syst."},{"issue":"1","key":"2021022709544732800_CIT0002","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s00170-016-8410-1","article-title":"An Efficient Adaptive Dispatching Method for Semiconductor Wafer Fabrication Facility","volume":"84","author":"Li","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"8","key":"2021022709544732800_CIT0003","doi-asserted-by":"crossref","first-page":"2118","DOI":"10.1080\/00207543.2011.575090","article-title":"A Batching and Scheduling Algorithm for the Diffusion Area in Semiconductor Manufacturing","volume":"50","author":"Yugma","year":"2012","journal-title":"Int. J. Prod. Res."},{"issue":"2","key":"2021022709544732800_CIT0004","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/TSM.2018.2826530","article-title":"Job Dispatch Control for Production Lines With Overlapped Time Window Constraints","volume":"31","author":"Wang","year":"2018","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"2021022709544732800_CIT0005","first-page":"3543","article-title":"Analyzing Different Dispatching Policies for Probability Estimation in Time Constraint Tunnels in Semiconductor Manufacturing","author":"Lima","year":"2017"},{"issue":"2","key":"2021022709544732800_CIT0006","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s11081-015-9288-8","article-title":"Data Driven Construction of Convex Region Surrogate Models Optimization and Engineering","volume":"17","author":"Zhang","year":"2016","journal-title":"Optim. Eng."},{"key":"2021022709544732800_CIT0007","first-page":"1657","article-title":"Integer Programming-Based Real-Time Scheduler in Semiconductor Manufacturing","author":"Ham","year":"2009"},{"issue":"6","key":"2021022709544732800_CIT0008","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1007\/s10845-011-0571-z","article-title":"Scheduling Two-Stage Hybrid Flow Shops With Parallel Batch, Release Time, and Machine Eligibility Constraints","volume":"23","author":"Wang","year":"2012","journal-title":"J. Intell. Manuf."},{"issue":"3","key":"2021022709544732800_CIT0009","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s00170-007-1208-4","article-title":"Robust Production Control Policies Considering WIP Balance and Setup Time in a Semiconductor Fabrication Line","volume":"39","author":"Kim","year":"2008","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"1","key":"2021022709544732800_CIT0010","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.simpat.2013.03.006","article-title":"An Evolutionary Simulation-Based Optimization Approach for Dispatching Scheduling","volume":"35","author":"Korytkowski","year":"2013","journal-title":"Simul. Modell. Pract. Theory"},{"issue":"1","key":"2021022709544732800_CIT0011","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s00170-013-4775-6","article-title":"Dynamic Dispatching for Interbay Material Handling by Using Modified Hungarian Algorithm and Fuzzy-Logic-Based Control","volume":"67","author":"Qin","year":"2013","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"2","key":"2021022709544732800_CIT0012","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/TASE.2012.2221087","article-title":"Adaptive Dispatching Rule for Semiconductor Wafer Fabrication Facility","volume":"10","author":"Li","year":", 2013","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"6","key":"2021022709544732800_CIT0013","doi-asserted-by":"crossref","first-page":"785","DOI":"10.3724\/SP.J.1004.2009.00785","article-title":"A Survey of Data-Based Production Scheduling Methods","volume":"35","author":"Liu","year":"2009","journal-title":"Acta Autom. Sin."},{"issue":"13","key":"2021022709544732800_CIT0014","doi-asserted-by":"crossref","first-page":"3669","DOI":"10.1080\/00207540701846236","article-title":"Data-Mining-Based Dynamic Dispatching Rule Selection Mechanism for Shop Floor Control Systems Using a Support Vector Machine Approach","volume":"47","author":"Shiue","year":"2010","journal-title":"Int. J. Prod. Res."},{"issue":"6","key":"2021022709544732800_CIT0015","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.rcim.2009.04.015","article-title":"Application of Ant Colony, Genetic Algorithm and Data Mining-Based Techniques for Scheduling","volume":"25","author":"Kumar","year":"2009","journal-title":"Rob. Comput. Integr. Manuf."},{"key":"2021022709544732800_CIT0016","first-page":"1170","article-title":"Simulation-Based Solution of Load-Balancing Problems in the Photolithography Area of a Semiconductor Wafer Fabrication Facility","author":"M\u00f6nch","year":"2001"},{"volume-title":"A Heuristic Load Balancing Scheduling Method for Dedicated Machine Constraint. Advances in Applied Artificial Intelligence","year":"2006","author":"Shr","key":"2021022709544732800_CIT0017"},{"key":"2021022709544732800_CIT0018","first-page":"2731","article-title":"Dedication Load Based Dispatching Rule for Photolithograph Machines With Dedication Constraint","author":"Chung","year":"2016"},{"issue":"3","key":"2021022709544732800_CIT0019","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1109\/TCOMM.2014.012614.130747","article-title":"Towards Scalable Traffic Management in Cloud Data Centers","volume":"62","author":"Assi","year":"2014","journal-title":"IEEE Trans. Commun."},{"issue":"2","key":"2021022709544732800_CIT0020","doi-asserted-by":"crossref","first-page":"0210041","DOI":"10.1115\/1.4042325","article-title":"A Machine Learning Approach to Kinematic Synthesis of Defect-Free Planar Four-Bar Linkages","volume":"19","author":"Deshpande","year":"2019","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"3","key":"2021022709544732800_CIT0021","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1142\/S1793351X16500045","article-title":"Deep Learning","volume":"10","author":"Hao","year":"2016","journal-title":"Int. J. Semant. Comput."},{"issue":"1","key":"2021022709544732800_CIT0022","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"issue":"1","key":"2021022709544732800_CIT0023","doi-asserted-by":"crossref","first-page":"0110021","DOI":"10.1115\/1.4037434","article-title":"An Unsupervised Machine Learning Approach to Assessing Designer Performance During Physical Prototyping","volume":"18","author":"Dering","year":"2018","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"1","key":"2021022709544732800_CIT0024","first-page":"31","article-title":"Neural Network With Deep Learning Architectures","volume":"39","author":"Patel","year":"2018","journal-title":"J. Inf. Optim. Sci."},{"issue":"1","key":"2021022709544732800_CIT0025","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ymssp.2015.11.014","article-title":"Construction of Hierarchical Diagnosis Network Based on Deep Learning and Its Application in the Fault Pattern Recognition of Rolling Element Bearings","volume":"72","author":"Gan","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"issue":"1","key":"2021022709544732800_CIT0026","doi-asserted-by":"crossref","first-page":"011004","DOI":"10.1115\/1.4037435","article-title":"Application of Feature-Learning Methods Toward Product Usage Context Identification and Comfort Prediction","volume":"18","author":"Ghosh","year":"2018","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"4","key":"2021022709544732800_CIT0027","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","article-title":"Extreme Learning Machine for Multilayer Perceptron","volume":"27","author":"Tang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"2021022709544732800_CIT0028","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent Advances in Convolutional Neural Networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recogn."},{"issue":"10","key":"2021022709544732800_CIT0029","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A Search Space Odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"2021022709544732800_CIT0030","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation With Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"2021022709544732800_CIT0031","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2017.07.014","article-title":"A Hybrid MLP-CNN Classifier for Very Fine Resolution Remotely Sensed Image Classification","volume":"140","author":"Zhang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"issue":"1\u20132","key":"2021022709544732800_CIT0032","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00704-015-1702-9","article-title":"Landslide Susceptibility Assesssment in the Uttarakhand Area (India) Using GIS: A Comparison Study of Prediction Capability of Naive Bayes, Multilayer Perceptron Neural Networks, and Functional Trees Methods","volume":"128","author":"Pham","year":"2017","journal-title":"Theor. Appl. Climatol."},{"issue":"1","key":"2021022709544732800_CIT0033","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.catena.2016.09.007","article-title":"Hybrid Integration of Multilayer Perceptron Neural Networks and Machine Learning Ensembles for Landslide Susceptibility Assessment at Himalayan Area (India) Using GIS","volume":"149","author":"Pham","year":"2017","journal-title":"Catena"},{"issue":"2","key":"2021022709544732800_CIT0034","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial Prediction Models for Shallow Landslide Hazards: A Comparative Assessment of the Efficacy of Support Vector Machines, Artificial Neural Networks, Kernel Logistic Regression, and Logistic Model Tree","volume":"13","author":"Tien Bui","year":"2016","journal-title":"Landslides"},{"issue":"1","key":"2021022709544732800_CIT0035","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.dsp.2016.12.004","article-title":"Time-frequency Localized Three-Band Biorthogonal Wavelet Filter Bank Using Semidefinite Relaxation and Nonlinear Least Squares With Epileptic Seizure EEG Signal Classification","volume":"62","author":"Bhati","year":"2017","journal-title":"Digital Signal Process."},{"issue":"1","key":"2021022709544732800_CIT0036","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.icheatmasstransfer.2016.02.002","article-title":"Estimation of Thermal Conductivity of Al2O3\/Water (40%)-Ethylene Glycol (60%) by Artificial Neural Network and Correlation Using Experimental Data","volume":"74","author":"Hemmat Esfe","year":"2016","journal-title":"Int. Commun. Heat Mass Transfer"},{"issue":"1","key":"2021022709544732800_CIT0037","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.atmosenv.2015.02.030","article-title":"Artificial Neural Networks Forecasting of PM2.5 Pollution Using Air Mass Trajectory Based Geographic Model and Wavelet Transformation","volume":"107","author":"Feng","year":"2015","journal-title":"Atmos. Environ."},{"issue":"1","key":"2021022709544732800_CIT0038","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1016\/j.jhydrol.2015.09.028","article-title":"Modeling of Groundwater Level Fluctuations Using Dendrochronology in Alluvial Aquifers","volume":"529","author":"Gholami","year":"2015","journal-title":"J. Hydrol."},{"key":"2021022709544732800_CIT0039","doi-asserted-by":"crossref","DOI":"10.1109\/eStream.2017.7950313","article-title":"Single Sound Source Localization Using Multi-Layer Perceptron","author":"Sakavicius","year":"2017"},{"key":"2021022709544732800_CIT0040","first-page":"1","article-title":"Web Service Classification Using Multi-Layer Perceptron Optimized With Tabu Search","author":"Syed-Mustafa","year":"2015"},{"key":"2021022709544732800_CIT0041","first-page":"1","article-title":"Integrating Fundamental and Technical Analysis of Stock Market Through Multi-Layer Perceptron","author":"Namdari","year":"2018"},{"issue":"5","key":"2021022709544732800_CIT0042","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.12693\/APhysPolA.133.1281","article-title":"Delay Systems Synthesis Using Multi-Layer Perceptron Network","volume":"133","author":"Plonis","year":"2018","journal-title":"Acta Phys. Pol. A"},{"key":"2021022709544732800_CIT0043","first-page":"97","article-title":"The Study of Architecture MLP With Linear Neurons in Order to Eliminate the \u201cVanishing Gradient\u201d Problem","author":"Kolbusz","year":"2017"}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/doi\/10.1115\/1.4045742\/6649856\/jcise_20_3_031001.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/doi\/10.1115\/1.4045742\/6649856\/jcise_20_3_031001.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T09:58:32Z","timestamp":1614419912000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/doi\/10.1115\/1.4045742\/1072089\/A-Predictive-Dispatching-Rule-Assisted-by-Multi"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,17]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,6,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4045742","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"type":"print","value":"1530-9827"},{"type":"electronic","value":"1944-7078"}],"subject":[],"published":{"date-parts":[[2020,1,17]]},"article-number":"031001"}}