Design methodology for advanced manufacturing systems
Computers in Industry
A taxonomy of model abstraction techniques
WSC '95 Proceedings of the 27th conference on Winter simulation
Five simple principle of modelling
WSC '96 Proceedings of the 28th conference on Winter simulation
Effect of pruning and early stopping on performance of a boosting ensemble
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Using MLP networks to design a production scheduling system
Computers and Operations Research - Special issue: Emerging economics
A review on evolution of production scheduling with neural networks
Computers and Industrial Engineering
Simulation Reduction Models Approach Using Neural Network
UKSIM '08 Proceedings of the Tenth International Conference on Computer Modeling and Simulation
A prediction interval-based approach to determine optimal structures of neural network metamodels
Expert Systems with Applications: An International Journal
Training a neural network to select dispatching rules in real time
Computers and Industrial Engineering
Pruned neural networks for regression
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Intelligent supply chain management using adaptive critic learning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Neural-network construction and selection in nonlinear modeling
IEEE Transactions on Neural Networks
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Simulation is often used to evaluate supply chain or workshop management. This simulation task needs models, which are difficult to construct. The aim of this work is to reduce the complexity of a simulation model design. The proposed approach combines discrete and continuous approaches in order to construct speeder and simpler reduced model. The simulation model focuses on bottlenecks with a discrete approach according to the theory of constraints. The remaining of the workshop must be taken into account in order to describe how the bottlenecks are fed. It is modeled through a continuous approach thanks to a neural network. In particular, we use a multilayer perceptron. The structure of the network is determined by using a pruning procedure. For validation, this approach is applied to the modelisation of a sawmill workshop.