Title :
Hybrid genetic-VNS algorithm with total flowtime minimization for the no-wait flowshop problem
Author :
Yang, Ning ; Li, Xiao-ping ; Zhu, Jie ; Wang, Qian
Author_Institution :
Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing
Abstract :
In this paper, a hybrid genetic-VNS algorithm is proposed for the no-wait flowshop problem with total flowtime minimization. To avoid pitfalls of GA, such as poor local search capability and premature convergence, a rather effective VNS local search is introduced based on the framework of the improved GA. To fast convergence of the algorithm, ICH2 (an efficient composite heuristic) is used for the initial population generation. Experimental results show that the proposed algorithm outperforms other best two recent existing methods on both small and large instances.
Keywords :
flow shop scheduling; genetic algorithms; minimisation; composite heuristic; hybrid genetic-VNS algorithm; initial population generation; no-wait flowshop problem; scheduling problem; total flowtime minimization; Computer science; Convergence; Cybernetics; Genetic mutations; Job shop scheduling; Machine learning; Machine learning algorithms; Minimization methods; Stochastic processes; Thin film transistors; Hybrid Genetic-VNS algorithm; no-wait flowshop; scheduling; total flowtime;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620539