An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems
Küçük Resim Yok
Tarih
2011
Yazarlar
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Dergi ISSN
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ELSEVIER
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The hybrid flow shop scheduling with multiprocessor task (HFSMT) problem is a substantial production scheduling problem for minimizing the makespan, and there exist many difficulties in solving large scale HFSMT problems which include many jobs, machines and tasks. The HFSMT problems known as NP-hard and the proposal of an efficient genetic algorithm (GA) were taken into consideration in this study. The numerical results prove that the computational performance of a GA depends on the factors of initial solution, reproduction, crossover, and mutation operators and probabilities. The implementation details, including a new mutation operator, were described and a full factorial experimental design was determined with our GA program by using the best values of the control parameters and the operators. After a comparison was made with the studies of Oguz [1], Oguz and Ercan [2] and Kahraman et al. [3] related to the HFSMT problems, the computational results indicated that the proposed genetic algorithm approach is very effective in terms of reduced total completion time or makespan (C-max) for the attempted problems. (C) 2010 Elsevier B. V. All rights reserved.
Açıklama
Anahtar Kelimeler
Hybrid flow shop, Multiprocessor task scheduling problems, Genetic algorithm, Design of experiment
Kaynak
APPLIED SOFT COMPUTING
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
11
Sayı
3
