Prediction of simulated factory layout throughput using artificial intelligence
Abstract
The use of artificial neural networks for the optimisation of factory layouts is not a common practice, primarily due to the challenge of collecting sufficient layout data to form datasets for artificial intelligence (AI) model training. This paper presents a supervised learning method derived from a PhD thesis that employs neural networks to assess factory layouts. The training data is generated using a random layout algorithm, which is capable of producing numerous layouts. These layouts are then labeled through a discrete event simulation. The combination of layouts and simulation metrics serves as the training basis for the neural network. The AI framework integrates a convolutional neural network with a multilayer perceptron, which is capable of handling both tabular and image data. Ultimately, this allows us to calculate of the simulated throughput.
First published online 20 January 2025
Keyword : discrete simulation, AI-supported simulation, neural network, transportation, decision support systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
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