Optimization of manufacturing systems using a neural network metamodel with a new training approach
Küçük Resim Yok
Tarih
2009
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PALGRAVE MACMILLAN LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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. Journal of the Operational Research Society (2009) 60, 1191-1197. doi:10.1057/palgrave.jors.2602620 Published online 30 July 2008
Açıklama
Anahtar Kelimeler
simulation, metamodel, simulation optimization, neural networks, tabu search
Kaynak
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
60
Sayı
9
