Identifying the relative importance of predictive variables in artificial neural networks based on data produced through a discrete event simulation of a manufacturing environment. (2nd October 2019)

• Record Type: Journal Article
• Authors: Pires dos Santos, R.; Dean, D. L.; Weaver, J. M.; Hovanski, Y.
• Is Part Of: International journal of modelling & simulation. Volume 39:Number 4(2019)
• Page Start: 234
• Page End: 245
• Publication Date: 2019-10-02
• Subjects: Discrete event simulation (DES) -- artificial neural networks (ANNs) -- connection weight approach -- data mining; Mathematical models -- Periodicals; Simulation methods -- Periodicals; Mathematical models; Simulation methods; Periodicals; 003.3
• DOI: 10.1080/02286203.2018.1558736
• Languages: English
• ISSNs: 0228-6203

Abstract

ABSTRACT: This research used a discrete event simulation to create data on a shipment receiving process instead of using historical records on the process. The simulation was used to create records with different inputs and operating conditions and the resulting overall elapsed time for the overall process. The resulting records were used to create a set of predictive artificial neural network models that predicted elapsed time based on the process characteristics. Then, the connection weight approach was used to determine the relative importance of the input variables. The connection weight approach was applied in three different steps: (1) on all input variables to identify predictive and non-predictive inputs, (2) on all predictive inputs, and (3) after removal of a dominating predictive input. This produced a clearer picture of the relative importance of input variables on the outcome variable than applying the connection weight approach once.