Abstract
The hybrid algorithm proposed by Liou, Hsieh [1] is a combination between genetic algorithm and particle swarm optimization that can be applied to the flow shop scheduling with constraints of families, setup times, transportation times, batch processing in some machines, and postponement of activities at the end of the work day, F m |fmls, s i,k , t i,j,k , prmu, batch, day|C max . In this hybrid algorithm, each sequence is a chromosome and at the same time an individual of a swarm, individuals produce new generations and die (like in genetic algorithms), and the decision of which chromosomes will undergo the application of the mutation and crossover operators is made on the basis of the individuals gbest and lbest (like in particle swarm optimization). Finally, the algorithm was benchmarked using Taillard’s instances and was applied in a real case, eleven real sequences of production were measured and the C max decreased 16.45% on average, this is measured as the percent change of C max given by the algorithm with respect to the C max observed in the real case.
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Pomar, L.A., Cruz Pulido, E., Tovar Roa, J.D. (2017). A Hybrid Genetic Algorithm and Particle Swarm Optimization for Flow Shop Scheduling Problems. In: Figueroa-García, J., López-Santana, E., Villa-Ramírez, J., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2017. Communications in Computer and Information Science, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-319-66963-2_53
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