In this paper, we propose a hybrid developed ant colony (HDAC) optimization for solving the multi-objective integer partial flexible open shop scheduling problem.

In the hybrid algorithm, every nourishment sources is given by two vectors, i.e., the machine task vector and the operation scheduling vector. The developed ant is divided into three groups, namely, employed ants, onlookers, and scouts ants. Furthermore, an external developed archive set is introduced to record non-dominated solutions found so far. To balance the exploration and exploitation capability of the algorithm, the scout ants in the hybrid algorithm are divided into two parts. The scout ants in single part perform arbitrarily look in the predefined area while each scout ant in another part randomly choose one non-dominated solution from the developed archive set.

Experimental results on the notable benchmark instances and comparisons with other recently demonstrated algorithms show the proficiency and effectiveness Of the proposed algorithm.