Genetic Algorithms Hybridized with Greedy Algorithms and Local Search over the Spaces of Active and Semi-active Schedules

The Job Shop Scheduling is a paradigm of Constraint Satisfaction Problems that has interested to researchers over the last years. In this work we propose a Genetic Algorithm hybridized with a local search method that searches over the space of semi-active schedules and a heuristic seeding method that generates active schedules stochastically. We report results from an experimental study over a small set of selected problem instances of common use, and also over a set of big problem instances that clarify the influence of each method in the Genetic Algorithm performance.