Reduction of Product Driven System emulation models based on neural network: impact of discrete data
نویسندگان
چکیده
Product Driven Systems (PDS) architecture needs emulation systems [13]. Discrete events simulation is then often used to build this emulation tool, but emulation model design is not a trivial task. Also, the goal of this paper is the study of the design of a simulation model by reducing its complexity. According to theory of constraints, we want to build reduced models composed exclusively by bottlenecks and a neural network. Particularly a multilayer perceptron, is used. The structure of the network is determined by using a pruning procedure. This work focuses on the impact of discrete data on the results. This approach is applied to sawmill internal supply chain.
منابع مشابه
A neural network for the reduction of a Product Driven System emulation model
In new Intelligent Manufacturing Systems, Product Driven Systems (PDS) architectures require emulation tool (Thomas et al. 2008) to be developed. Discrete events simulation is often used to build such emulation tool, nevertheless this remains complex because of large scale problems. The goal of this paper is to propose a way to design a simulation model by reducing its complexity. According to ...
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