Abstract
Flow shop sequencing is one of the most well-known production scheduling problems and a typical NP-hard combinatorial optimization problem with strong engineering background. To efficiently deal with flow shop sequencing problems, an improved genetic algorithm using novel adaptive genetic operators is proposed. Researches are made in aspects such as problem modeling, encoding, decoding, crossover and mutation of genetic algorithms and so on. The proposed algorithm has been tested on scheduling problem benchmarks. Experimental results show that improved genetic algorithm is quite flexible with satisfactory results, and require fewer running time than pure genetic algorithms and simulated annealing
Published Version
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