Ant colony optimization using pheromone updating strategy to solve job shop scheduling

摘要

Scheduling is considered to be a major task to improve the shop-floor productivity. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the Ant Colony Optimization metaheuristic to job shop problem. The main characteristics of this model are positive feedback and distributed computation. The inspiring source of Ant Colony Optimization is pheromone trail laying and following behavior of real ant. The methods of updating the pheromone have more influence in solving instances of job shop problem. An algorithm is introduced to improve the basic ant colony system by using a pheromone updating strategy. Experiments using well-known bench mark problems show that this approach improves on the performance obtained by the basic ant colony system.