A genetic algorithm based approach to flowshop scheduling

Flowshop scheduling deals with processing a set of jobs through a set of machines, where all jobs have to pass among machines in the same order. To solve this scheduling problem, an adaptive genetic algorithm is developed. The probability of crossover and mutation is dynamically adjusted according to the individual's fitness value. The individuals with higher fitness values are assigned to lower probabilities of genetic operator, and vice versa. The computational results show that the modified genetic algorithm has effective convergence and efficient computation speed compared to the basic genetic algorithm.