The construction industry greatly benefits from the utilization of heavy machines and equipment to accomplish successful projects. Tower cranes, in specific, have a crucial role in the transportation of material loads across the site. Because these machines are fixed to the ground, it is essential for planners and managers to position them in a location that provides the most efficient transfer of materials possible. This is commonly known as the tower crane allocation problem, and many researchers have attempted to optimize the tower crane location using mathematical, artificial intelligence, and simulation approaches. However, many works from the literature contain critical errors which make the models infeasible. This research presents the application of ant colony optimization (ACO) and an ACO variation to tower crane allocation models. Results show that the approaches presented in this work are up to par with even the most powerful methodologies used to solve the problem
