The problem of scheduling in permutation flow shop with the objective of minimizing the maximum completion time, or makespan, is considered. A new ant colony optimization algorithm is developed for solving the problem. A novel mechanism is employed in initializing the pheromone trails based on an initial sequence. Moreover, the pheromone trail intensities are limited between lower and upper bounds which change dynamically. When a complete sequence of jobs is constructed by an artificial ant, a local search is performed to improve the performance quality of the solution. The proposed ant colony algorithm is applied to Taillard’s benchmark problems. Computational experiments suggest that the algorithm yields better results than well-known ant colony optimization algorithms available in the literature.
