NDLI: An hybrid heuristic using genetic algorithm and simulated annealing algorithm to solve machine loading problem in FMS

Content Provider	IEEE Xplore Digital Library
Author	Yogeswaran, M. Ponnambalam, S.G. Tiwari, M.K.
Copyright Year	2007
Description	Author affiliation: Monash Univ., Bandar Sunway (Yogeswaran, M.; Ponnambalam, S.G.)
Abstract	A machine loading problem in flexible manufacturing system (FMS) is discussed with bicriterion objectives of minimizing system unbalance and maximizing system throughput in the occurrence of technological constraints such as available machining time and tool slots. An efficient evolutionary algorithm by hybridizing the genetic algorithm (GA) and simulated annealing (SA) algorithm called GASA is proposed in this paper. The performance of the GASA is tested by using 10 sample dataset and the results are compared with the heuristics reported in the literature. Two machine selection heuristics are proposed and their influence on the quality of the solution is also studied. Extensive computational experiments have been carried out to evaluate the performance of the proposed evolutionary heuristics and the results are presented in tables and figures. The results clearly support the better performance of GASA over the algorithms reported in the literature.
Starting Page	182
Ending Page	187
File Size	602590
Page Count	6
File Format	PDF
ISBN	9781424411535
DOI	10.1109/COASE.2007.4341779
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Simulated annealing Flexible manufacturing systems Throughput Evolutionary computation Manufacturing automation Genetic engineering USA Councils Machining Testing
Content Type	Text
Resource Type	Article

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