One of the tools in the gamut of global optimization search procedures is ant algorithms, inspired by the behaviour of the well-known insects - ants. Natural ant colonies exhibit ad-hoc decision-making processes in their day-to-day living activities, such as foraging and brooding. These processes could be modelled and used as tools to solve many practical scheduling problems that are present in current manufacturing environments. This paper proposes web-based ant colony system algorithm (WACSA) optimization procedures to solve several real-world manufacturing systems problems. The problems considered are: (1) single-machine scheduling optimization considering tool wear; (2) drilling sequence optimization; and (3) single-machine scheduling considering total job changeover cost. Results indicate that WACSA provides an optimal solution quickly. It also shows that the ant algorithm is preferred over existing meta-heuristics, as it provides a high level of scheduling flexibility.