A genetic algorithm for robust schedules

Computing a schedule for a given single machine problem is often difficult for irregular criteria, but when the data are uncertain, the problem is much more complicated. In this paper, we create a genetic algorithm to compute robust schedules when release dates are subject to small variations. Instead of evaluating a single fitness function at each iteration, several functions are evaluated taking into account the variation of the data. This method leads to robust solutions, meaning that the value of the objective function remains high when small variations in some release dates occurs.