A nested ant colony algorithm for hybrid production scheduling

The effectiveness of ant colony algorithms as powerful tools to solve complicated optimization problems has been demonstrated in a number of applications. Due to their biological background, these methods are usually used to solve difficult combinatorial optimization problems and are seldom adopted in continuous-space search. A nested ant colony algorithm is presented in this paper to solve production scheduling problems in which the search must be done simultaneously in integer-spaces, real-spaces, and discrete-sequence-spaces. A novel pheromone update algorithm is suggested to promote the search efficiency. The performance of the proposed algorithm in solving such kind of difficult optimization problems is demonstrated via simulation experiments for a practical flexible production line.