The purpose of this research is to investigate one well-known type of scheduling problem, the Permutation Flow-Shop Scheduling Problem, with the makespan as the objective function to be minimized. During the last four decades, the permutation flow-shop scheduling problem has received considerable attention. Various techniques, ranging from the simple constructive algorithm to the state-of-the-art techniques, such as Genetic Algorithms, have been proposed for this scheduling problem.; The development of a solution methodology based on genetic algorithms, yielding (near) optimal makespans, has been investigated in this thesis. In order to improve the performance of the search technique, the proposed genetic algorithm is hybridized with an Iterated Greedy Search Algorithm.; The parameters of both the hybrid and the non-hybrid genetic algorithms were tuned using the Full Factorial Experimental Design and Analysis of Variance. The performance of the tuned hybrid and non-hybrid algorithms are finally examined on the existing standard benchmark problems cited in the literature, and it is shown that the proposed hybrid genetic algorithm performs well on those benchmark problems. In addition, it is demonstrated that the hybrid proposed algorithm is robust with respect to problem parameters, such as population size, crossover type, and crossover probability.
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