This paper studies the problem of assigning tasks to a workforce with different skills. The problem is modeled as an unrelated parallel scheduling problem, incorporating sequence-dependent setup times (UPMSPSDST). Exact methods generally are not able to solve real large problems of UPMSPSDST. Hence, this research introduces an efficient, straightforward metaheuristic solution leveraging the Ant Colony Optimization (ACO) algorithm. The objective is to minimize the total completion time while assigning jobs to unrelated parallel machines with sequence-dependent preparation times. The algorithm establishes a threshold for improving the Ants (solutions) to select only promising ants for the improvement phase, thereby reducing the computational effort performed by local search operators. The proposed ACO algorithm maintains a basic structure and could be extended to solve other scheduling problems. A set of test instances available in the literature has been used to validate the efficiency of the proposed methodology. In addition, the results have been compared with the best previously published works. The ACO algorithm improves 30% of the best-known solutions (BKS) and reaches 30% of the BKS. The results show that the average performance of the ACO algorithm exceeds the average performance of the methods used by the best previously published works for the UPMSPSDST.