Simulation metamodeling with neural networks

Date
1997
Editor(s)
Advisor
Sabuncuoğlu, İhsan
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
Print ISSN
Electronic ISSN
Publisher
Bilkent University
Volume
Issue
Pages
Language
English
Journal Title
Journal ISSN
Volume Title
Series
Abstract

Modern manufacturing environments increasingly call for more sophisticated cind fast decision aiding systems for their management. Artificial neural networks have been proposed as an alternative cipproach for formalizing various quantitative and qualitative aspects of manufacturing systems. This research attempts to lay down the motivation behind using neural networks as a simulation metamodeling approach. This research can be classified under the major headings of simulation metamodeling for the purpose of estimating system performance. Steiidy state perfornuince of non-terminating type systems and transient state performance of terminating tyj^e systems are examined under job shop environments by applying Back Propagation neural networks. We attempt to study the peribrrnance of neural metamodels with respect to estimating two performance measures (mean machine utilization and mean job tardiness), with respect to system complexity, with different types of system configurations (deterministic cuid stochastic), with respect to multiple metamodel accuracy assessment criteria and various metamodel design settings. The objective of this analysis is to investigate the potential application of neural metamodeling.

Course
Other identifiers
Book Title
Citation
Published Version (Please cite this version)