© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Optimizing aircraft maintenance, repair and overhaul (MRO) planning, i.e., reviewing aircraft history based on when and where the aircraft should be serviced, is essential to ensuring aviation safety and operations. This paper examines a method for optimizing the scheduling of MRO operations for complex products. It has been shown in the literature that scheduling optimization of MRO operations is an NP-hard problem. In this study, the planning problem for MRO service resources is first analyzed, a mathematical model for the MRO process planning problem is established, and a multi-objective mathematical model for minimizing makespan time, maintenance time, and cost is proposed to represent the planning problem. Then, to optimize the makespan time, maintenance time, and cost, we first generate the maintenance, repair and overhaul transaction path (MROTP) data to utilize the PLM system efficiently, which ensures the effective operation of MRO actions throughout the aircraft lifecycle. MROTP data are obtained with engineering studies in our proposed research, and received data are retained in the PLM system. Finally, the modified ACO algorithm that performs scheduling optimization of the MRO process and the integration process of PLM with MRO operations is presented. The proposed algorithm is tested and validated by simulations in MATLAB software with the data obtained from the leading MRO company in Turkey. The Gurobi Solver is used to obtain the optimal solution by converting the problem into mixed-integer linear programming (MILP). The experimental setup is run using Gurobi with a 1-hour time limit, and the best solution is considered with an average run time calculated in seconds. When solving complicated instances, the MILP model-based approach is challenging. The study and comparison of the findings demonstrate that the proposed ACO-based algorithms have an advantage in planning MRO operations. The proposed ACO algorithm performs better than the other metaheuristic algorithms and the MILP model regarding maintenance time and cost.