Applying Genetic Algorithms to the Identical Parallel Machine Scheduling Problem

We are proposing an evolutionary approach to the identical parallel machine scheduling problem using a novel representation for genetic algorithms. The genetic algorithm is compared to the mathematical model of the problem. Both of the approaches are implemented in Python using related libraries. The results show that, specially for the larger instances of the problem, the genetic algorithm performs better; it finds an appropriate solution with low tardiness values in minutes, rather than hours when the mathematical model is used. Our study shows that genetic algorithms a valid approach for finding solutions to the identical parallel machine scheduling problem.