An adaptive learning approach for no-wait flowshop scheduling problems to minimize makespan
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ATLANTIS PRESS
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
No-wait flowshop scheduling problem (NW-FSSP) with the objective to minimize the makespan is an important sequencing problem in the production plans and applications of no-wait flowshops can be found in several industries. In a NW-FSSP, jobs are not allowed to wait between two successive machines. The NW-FSSPs are addressed to minimize makespan and the NW-FSSP is known as a NP- Hard problem. In this study, Agarwal et al.'s(1) adaptive learning approach (ALA) is improvement for NW-FSSPs. Improvements in adaptive learning approach is similar to neural-network training. The improvement adaptive learning approach (IALA) is applied to all of the 192 problems. The proposed IALA method for NW-FSSP is compared with Aldowaisan and Allahverdi's(2) results by using Genetic heuristic. The results of computational experiments on randomly generated NW-FSSPs are show that the proposed adaptive learning approach performs quite well.
Açıklama
Anahtar Kelimeler
No-wait flowshop, Adaptive learning approach, Genetic algorithm, Makespan
Kaynak
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
4
Sayı
4
