A Genetic Algorithm with Local Search for Solving Job Shop Problems

Authors:
L. W. Cai;Q. H. Wu;Z. Z. Yong
Affiliations:
-;-;-
Venue:
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Year:
2000

Citing 3
Cited 0

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Abstract

This paper presents a genetic algorithm specially designed for job shop problems. The algorithm has a simple coding scheme and new crossover and mutation operators. A simple local search scheme is incorporated in the algorithm leading to a combined genetic algorithm (CGA). It is evaluated in three famous Muth and Thompson problems (i.e. MT6×6, MT10×10, MT20×5). The simulation study shows that this algorithm possesses high efficiency and is able to find out the optimal solutions for the job shop problems.