Due-date setting and priority sequencing in a multiclass M/G.1 queue
Management Science
Single facility due date setting with multiple customer classes
Management Science
Management Science
The nature of statistical learning theory
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Management Science
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Support Vector Machine Regression for Volatile Stock Market Prediction
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
A Simple, Robust Leadtime-Quoting Policy
Manufacturing & Service Operations Management
Support Vector Machine for Regression and Applications to Financial Forecasting
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Supply chain planning: using simulation to evaluate buffer adjustment methods in order promising
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Travel-time prediction with support vector regression
IEEE Transactions on Intelligent Transportation Systems
Expert Systems with Applications: An International Journal
Traffic safety forecasting method by particle swarm optimization and support vector machine
Expert Systems with Applications: An International Journal
Computers and Operations Research
Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems
Computers and Industrial Engineering
Robotics and Computer-Integrated Manufacturing
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In a make-to-order production system, a due date must be assigned to new orders that arrive dynamically, which requires predicting the order flowtime in real-time. This study develops a support vector regression model for real-time flowtime prediction in multi-resource, multi-product systems. Several combinations of kernel and loss functions are examined, and results indicate that the linear kernel and the @e-insensitive loss function yield the best generalization performance. The prediction error of the support vector regression model for three different multi-resource systems of varying complexity is compared to that of classic time series models (exponential smoothing and moving average) and to a feedforward artificial neural network. Results show that the support vector regression model has lower flowtime prediction error and is more robust. More accurately predicting flowtime using support vector regression will improve due-date performance and reduce expenses in make-to-order production environments.