Des fourmis pour le problème d'ordonnancement de voitures

The car sequencing problem involves scheduling cars along an assembly line while satisfying capacity constraints. In this paper, we describe an Ant Colony Optimization (ACO) algorithm for solving this problem, and we propose two complementary pheromone structures for this algorithm : the rst one aims at learning good sub-sequences of cars, whereas the second one aims at learning "critical" cars. We experimentally compare these two pheromone structures, that have complementary performances, and show that their combination improves performances of each of them. We nally compare the ACO algorithm that combines the two pheromone structures with state-of-the-art approaches for this problem, and show that it allows to solve quicker most instances.