The paper is a review of the present-day problem about the logistic optimization of both two-dimensional and three-dimensional warehouses with m cranes. A tuple of heuristic algorithms is proposed to solve the NP-hard problem with minimizing the total execution time of a set of jobs using m cranes. The described optimization approaches are applicable to both two- and three-dimensional warehouses. As part of the numerical results, results regarding the solution of applied problems are shown. The ability to train the proposed model allows one to select the best heuristic settings for a specific warehouse based on real world data sets. Another important feature of the model is the ability to distribute the power and parallel execution of three independent processes: the ant colony optimization algorithm to construct a successful sequence of jobs, the algorithm to search the optimal set of heuristic weights of ki, and the algorithm to solve the planning problem.