Artificial neural network-based decision support systems in manufacturing processes: A systematic literature review
F Mumali - Computers & Industrial Engineering, 2022 - Elsevier
The use of artificial neural network models to enrich the analytical and predictive capabilities
of decision support systems in manufacturing has increased. The growing complexity and …
of decision support systems in manufacturing has increased. The growing complexity and …
Simulation for manufacturing system design and operation: Literature review and analysis
A Negahban, JS Smith - Journal of manufacturing systems, 2014 - Elsevier
This paper provides a comprehensive review of discrete event simulation publications
published between 2002 and 2013 with a particular focus on applications in manufacturing …
published between 2002 and 2013 with a particular focus on applications in manufacturing …
Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
This paper introduces an innovative physics-informed deep learning framework for
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …
Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling
Accurate prediction of building's response subjected to earthquakes makes possible to
evaluate building performance. To this end, we leverage the recent advances in deep …
evaluate building performance. To this end, we leverage the recent advances in deep …
Prediction of wind pressure coefficients on building surfaces using artificial neural networks
Knowing the pressure coefficient on building surfaces is important for the evaluation of wind
loads and natural ventilation. The main objective of this paper is to present and to validate a …
loads and natural ventilation. The main objective of this paper is to present and to validate a …
Comparing LSTM and GRU models to predict the condition of a pulp paper press
The accuracy of a predictive system is critical for predictive maintenance and to support the
right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable …
right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable …
Application and characterization of metamodels based on artificial neural networks for building performance simulation: A systematic review
In most of the countries, buildings are often one of the major energy consumers, leading to
the necessity of achieving sustainable building designs, and to the mandatory use of …
the necessity of achieving sustainable building designs, and to the mandatory use of …
The use of a grey-based Taguchi method for optimizing multi-response simulation problems
Simulation modelling is a widely accepted tool in system design and analysis, particularly
when the system or environment has stochastic and nonlinear behaviour. However, it does …
when the system or environment has stochastic and nonlinear behaviour. However, it does …
A systematic comparison of metamodeling techniques for simulation optimization in decision support systems
Simulation is a widely applied tool to study and evaluate complex systems. Due to the
stochastic and complex nature of real world systems, simulation models for these systems …
stochastic and complex nature of real world systems, simulation models for these systems …
Improving the efficiency of a Savonius wind turbine by designing a set of deflector plates with a metamodel-based optimization approach
Savonius wind turbines are the most suitable devices used in urban areas to produce
electrical power. This is due to their simplicity, ease of maintenance, and acceptable power …
electrical power. This is due to their simplicity, ease of maintenance, and acceptable power …