Fuzzy dynamic-prioritization agent-based system for forecasting job cycle time in a wafer fabrication plant

A fuzzy dynamic-prioritization agent-based system was developed in this study to improve the forecasting of the cycle time of a job in a wafer fabrication plant (wafer fab). In this system, multiple fuzzy agents forecast the cycle time of a job from various viewpoints, after which the aggregation and evaluation agent aggregates these fuzzy cycle time forecasts using an innovative operator (i.e., the fuzzy weighted intersection) into a single representative value. Subsequently, the optimization agent varies the authority levels of the fuzzy cycle time forecasting agents to optimize the forecasting performance. A practical example was used to evaluate the effectiveness of the fuzzy dynamic-prioritization agent-based system. The experiment results indicated that the fuzzy dynamic-prioritization agent-based system outperformed three rival methods in improving forecasting accuracy. In addition, the forecasting performance could be enhanced by discriminating the authority levels of the fuzzy cycle time forecasting agents.

[1]  Cristina Venera Geambasu,et al.  BPMN VS. UML ACTIVITY DIAGRAM FOR BUSINESS PROCESS MODELING , 2012 .

[2]  Demetris Stathakis,et al.  How many hidden layers and nodes? , 2009 .

[3]  W.L. Pearn,et al.  Due-Date Assignment for Wafer Fabrication Under Demand Variate Environment , 2007, IEEE Transactions on Semiconductor Manufacturing.

[4]  Wenjun Chris Zhang,et al.  Big data driven cycle time parallel prediction for production planning in wafer manufacturing , 2018, Enterp. Inf. Syst..

[5]  Pei-Chann Chang,et al.  A Case-Based Reasoning Approach for Due-Date Assignment in a Wafer Fabrication Factory , 2001, ICCBR.

[6]  Zbigniew Michna,et al.  Lead Times and Order Sizes - A not so Simple Relationship , 2015, ISAT.

[7]  Madan M. Gupta,et al.  Fuzzy mathematical models in engineering and management science , 1988 .

[8]  Bram Vanderborght,et al.  Attitudes of Factory Workers towards Industrial and Collaborative Robots , 2017, HRI.

[9]  W. Zhu,et al.  An independent study of two deep learning platforms - H2O and SINGA , 2016, 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[10]  Toly Chen,et al.  Advanced 3D printing technologies for the aircraft industry: a fuzzy systematic approach for assessing the critical factors , 2019 .

[11]  Max Tegmark,et al.  Why Does Deep and Cheap Learning Work So Well? , 2016, Journal of Statistical Physics.

[12]  Xiaoming Xu,et al.  Enhancing the convergence efficiency of a self-propelled agent system via a weighted model. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Filip Radlinski,et al.  A support vector method for optimizing average precision , 2007, SIGIR.

[14]  Toly Chen.  A job-classifying and data-mining approach for estimating job cycle time in a wafer fabrication factory , 2012 .

[15]  Hsin-Chieh Wu,et al.  Forecasting the unit cost of a DRAM product using a layered partial-consensus fuzzy collaborative forecasting approach , 2020 .

[16]  Vladik Kreinovich,et al.  Why Deep Neural Networks: A Possible Theoretical Explanation , 2018 .

[17]  Xu Tan,et al.  Analysis of production cycle-time distribution with a big-data approach , 2020, J. Intell. Manuf..

[18]  Toly Chen,et al.  An effective fuzzy collaborative forecasting approach for predicting the job cycle time in wafer fabrication , 2013, Comput. Ind. Eng..

[19]  Zheng Chongxun,et al.  The Use of Fuzzy BackPropagation Neural Networks for the Early Diagnosis of Hypoxic Ischemic Encephalopathy in Newborns , 2011, Journal of biomedicine & biotechnology.

[20]  Vincent Hargaden,et al.  Machine learning technologies for order flowtime estimation in manufacturing systems , 2019 .

[21]  Junliang Wang,et al.  A Data Driven Cycle Time Prediction With Feature Selection in a Semiconductor Wafer Fabrication System , 2018, IEEE Transactions on Semiconductor Manufacturing.

[22]  Aysenur Budak,et al.  An RFID-based tracking system for denim production processes , 2017 .

[23]  Yu Hu,et al.  Investigation of deep neural networks (DNN) for large vocabulary continuous speech recognition: Why DNN surpasses GMMS in acoustic modeling , 2012, 2012 8th International Symposium on Chinese Spoken Language Processing.

[24]  Hwa Jen Yap,et al.  Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy , 2012, Neurocomputing.

[25]  Toly Chen,et al.  Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM–FBPN-ensemble approach with multiple buckets and partial normalization , 2009 .

[26]  Botond Kádár,et al.  Manufacturing Lead Time Estimation with the Combination of Simulation and Statistical Learning Methods , 2016 .

[27]  Pei-Chann Chang,et al.  Fuzzy Back-Propagation Network for PCB Sales Forecasting , 2005, ICNC.

[28]  Hare Krishna Mohanta,et al.  Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network. , 2015, ISA transactions.

[29]  Ji-Bo Wang,et al.  Common due date assignment scheduling for a no-wait flowshop with convex resource allocation and learning effect , 2018, Engineering Optimization.

[30]  Jie Zhang,et al.  Big data analytics for forecasting cycle time in semiconductor wafer fabrication system , 2016 .

[31]  Toly Chen,et al.  A Fuzzy-Neural Ensemble and Geometric Rule Fusion Approach for Scheduling a Wafer Fabrication Factory , 2013 .

[32]  Toly Chen.  An intelligent mechanism for lot output time prediction and achievability evaluation in a wafer fab , 2008, Comput. Ind. Eng..

[33]  László Monostori,et al.  Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer , 2018 .

[34]  Amy J. C. Trappey,et al.  Development of an intelligent agent system for collaborative mold production with RFID technology , 2009 .

[35]  Kenji Suzuki,et al.  Artificial Neural Networks - Industrial and Control Engineering Applications , 2011 .

[36]  T. Martin McGinnity,et al.  Client-Server Architecture for Collaborative Remote Experimentation , 2005, Third International Conference on Information Technology and Applications (ICITA'05).

[37]  Yunrui Wang,et al.  Model construction of planning and scheduling system based on digital twin , 2020, The International Journal of Advanced Manufacturing Technology.

[38]  K. Jha,et al.  Cycle time reduction in outsourcing process: case of an Indian aerospace industry , 2020 .

[39]  Toly Chen,et al.  A Hybrid SOM-BPN Approach to Lot Output Time Prediction in a Wafer Fab , 2006, Neural Processing Letters.

[40]  Surjya K. Pal,et al.  Monitoring of drill flank wear using fuzzy back-propagation neural network , 2007 .

[41]  S. Kitayama,et al.  Multi-objective optimization for minimizing weldline and cycle time using variable injection velocity and variable pressure profile in plastic injection molding , 2020 .

[42]  Dusan Marcek,et al.  Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics , 2018 .

[43]  Junliang Wang,et al.  Bilateral LSTM: A Two-Dimensional Long Short-Term Memory Model With Multiply Memory Units for Short-Term Cycle Time Forecasting in Re-entrant Manufacturing Systems , 2018, IEEE Transactions on Industrial Informatics.

[44]  Toly Chen,et al.  A piecewise linear fgm approach for efficient and accurate fahp analysis: Smart backpack design as an example , 2020 .

[45]  Samuel Lukas,et al.  Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network , 2012, 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation.

[46]  Chi-Wei Lin,et al.  A fuzzy collaborative forecasting approach considering experts' unequal levels of authority , 2020, Appl. Soft Comput..

[47]  Tharam S. Dillon,et al.  Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming , 2011, Appl. Soft Comput..

[48]  Toly Chen,et al.  Incorporating the FCM-BPN approach with nonlinear programming for internal due date assignment in a wafer fabrication plant , 2010 .

[49]  Toly Chen,et al.  CART–BPN approach for estimating cycle time in wafer fabrication , 2015, J. Ambient Intell. Humaniz. Comput..

[50]  Bernard De Baets,et al.  Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions , 2006, Fuzzy Sets Syst..

[51]  Yunong Zhang,et al.  Link Between and Comparison and Combination of Zhang Neural Network and Quasi-Newton BFGS Method for Time-Varying Quadratic Minimization , 2013, IEEE Transactions on Cybernetics.

[52]  Toly Chen,et al.  A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory , 2017, J. Intell. Manuf..

[53]  Toly Chen,et al.  An advanced fuzzy collaborative intelligence approach for fitting the uncertain unit cost learning process , 2018, Complex & Intelligent Systems.

[54]  Toly Chen.  A fuzzy back-propagation network approach for planning actions to shorten the cycle time of a job in dynamic random access memory manufacturing , 2015, Neural Computing and Applications.

[55]  Toly Chen,et al.  A collaborative fuzzy-neural approach for internal due date assignment in a wafer fabrication plant , 2011 .

[56]  Toly Chen.  A fuzzy back propagation network for output time prediction in a wafer fab , 2003, Appl. Soft Comput..

[57]  Amr Badr,et al.  Forecasting of nonlinear time series using ANN , 2017 .