Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling Problems

There are different suggested values to adapt the basic parameters of a Genetic Algorithm, however, these values may not be the optimal for all kinds of applications. The following research presents a metaheuristic based on a Genetic Algorithm to solve problems of type Flow Shop Scheduling with the objective of minimizing the completion time of all jobs, known in literature as makespan or Cmax. This problem is typical of combinatorial optimization and can be found in manufacturing environment, where there are conventional machines-tools and different types of pieces which share the same route. A set of crossover and selection operators are implemented methods for the pro posed Genetic Algorithm once its main factors are calibrated, the size of the population, number of generations, mutation and crossover factors, statistical study is performed in order to determine the combinations of these parameters that has a greater influence. Finally, the combination of parameters whit the best performance is tested with problems of different levels of complexity in order to obtain satisfactory results in terms of solutions quality