## 1 Introduction  
Maintenance, Repair, and Overhaul (MRO) is a crucial sector in the remanufacturing industry [1]. In the context of Industrie 4.0, predictive maintenance has gained much attention in maintenance and asset management. Adopting reliability methods are one of the most interesting aspects to be considered. The works of [2; 3] proposed a novel model for inspection scheduling of welded components as well as additive manufacturing parts. This marks a clear pathway of interest in the scientific community that evolves to the industrial domain in enhancements of products and processes. Another aspect of MRO industry is on job scheduling of MRO parts which are typically low-process volume, high-value and complex components such as engine blades, compressor blisks and turbine disks. An engine blade, for example, its MRO processes generally consist of four fundamental phases comprising pre-treatment, material deposit, recontouring, and post-treatment. Various methods and machines can be utilized in each phase of the aforesaid sequence. Furthermore, due to the diversity of damages on components, the MRO process of each component might have to adapt to the sequence of these fundamental steps fully or partly and the processing time for each operation also varies due to this uncertainty. Therefore, scheduling of MRO processes is significantly different from conventional manufacturing processes. Some characteristics of MRO processes that scheduling task should take into accounts are disassembly process, uncertainty of material recovery, material matching requirements, stochastic routings and variable processing times [4]. In order to solve the scheduling optimization problem, numerous approaches and algorithms have been proposed and developed such as swarm intelligence algorithms, genetic algorithms, state-spacesearch algorithms, GraphPlan algorithm and critical path method [5]. Among these approaches, the swarm intelligence algorithms such as Ant Colony Optimization (ACO), Fish Swarm Optimization, Bee-Inspired Algorithms and Bacterial Foraging Optimization have been demonstrated as effective methods to solve complex optimization problems [6]. The most prevalent swarm intelligence algorithm is ACO that has been successfully implemented for a variety of optimization problems such as scheduling, routing, assignment and machine learning [7]. The implementation of the ACO algorithm does not require a complex mathematical model of the problem. Furthermore, the complexity of process routings in MRO processes can be straightforwardly defined in the ACO algorithm. Research on scheduling problem of MRO processes is scarce in the literature. Among the limited results, simulation optimization was usually adopted to formulate scheduling problem [4; 5; 6; 7; 8]. [PERSON] et al. [4] used hybrid algorithms based on nested partition framework to address stochastic routings and variable processing times. Multi-fidelity optimization based on ordinal transformation and optimal sampling proposed by [PERSON] et al. [8] was shown to achieve feasible solutions with low tardiness. [PERSON] et al. [9] implemented the genetic algorithm to optimize the scheduling of MRO resources considering the complexity of products. However, to the best of our knowledge, there is no paper that addresses scheduling MRO processes using swarm intelligence algorithms. In this paper, we adopt the ACO algorithm to solve the scheduling optimization for MRO processes in the Industrie 4.0 context. According to [PERSON], et al. [10], in order to adopt Industrie 4.0, digital systems and applications must be able to address several challenges, including complex variation requirements of production processes. Therefore, the ACO algorithm developed in this paper considers the complexity of process routings, variable process times and rescheduling. A use-case is defined reflecting the complexity of process routings and variable processing time MRO processes. Rescheduling is also included for illustrating the dynamic scheduling capacity of the algorithm.