Ant Colony Optimization for Job Shop Scheduling Problem Using Priority Rules

Scheduling problems have a vital role in recent years due to the growing consumer demand for variety, reduced product life cycles, changing markets with global competition and rapid development of new technologies. The Job Shop Scheduling Problem is one of the most popular scheduling models existing in practice, which is among the hardest combinatorial optimization problems. The Ant Colony Optimization is a technique of swarm intelligence, which is applied to combinatorial optimization problems as JSSP. This paper presents ACO meta-heuristic approach with new strategies in solving JSSP. Priority rules play a major role during the construction of a solution. Different priority rules are analyzed and the best one is found. Experiments using well-known benchmark problems show that this approach improves the performance obtained by the basic ant colony system.