Optimization of manufacturing systems using a neural network metamodel with a new training approach
Journal of the Operational Research Society, cilt.60, sa.9, ss.1191-1197, 2009 (SCI-Expanded, SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 60 Sayı: 9
- Basım Tarihi: 2009
- Doi Numarası: 10.1057/palgrave.jors.2602620
- Dergi Adı: Journal of the Operational Research Society
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
- Sayfa Sayıları: ss.1191-1197
- Anahtar Kelimeler: Metamodel, Neural networks, Simulation, Simulation optimization, Tabu search
- Maltepe Üniversitesi Adresli: Evet
Özet
In this study, two manufacturing systems, a kanban-controlled system and a multi-stage, multi-server production line in a diamond tool production system, are optimized utilizing neural network metamodels (tst-NNM) trained via tabu search (TS) which was developed previously by the authors. The most widely used training algorithm for neural networks has been back propagation which is based on a gradient technique that requires significant computational effort. To deal with the major shortcomings of back propagation (BP) such as the tendency to converge to a local optimal and a slow convergence rate, the TS metaheuristic method is used for the training of artificial neural networks to improve the performance of the metamodelling approach. The metamodels are analysed based on their ability to predict simulation results versus traditional neural network metamodels that have been trained by BP algorithm (bp-NNM). Computational results show that tst-NNM is superior to bp-NNM for both of the manufacturing systems. © 2009 Operational Research Society Ltd.