## 5 Conclusions  
In this paper, motivated by the Industrie 4.0 concept, in which the complex variations of production processes must be systematically addressed, we have presented a solution of scheduling MRO processes. Our approach is based on the well-known ACO algorithm enhancing its parameters to optimize the schedule of MRO processes. Our approach was also proven to show good performance for dynamic scheduling scenarios and has been tested in Model Factory @ARTC.  
In response to the state-of-the-art research gap where implementing ACO algorithm for MRO environment is scarce, the contribution of this research is on developing and studying an ACO algorithm for an MRO use-case with two business objectives which are minimizing the total scheduling time and tardiness of all jobs. The developed algorithm also has a capacity of dynamic scheduling. Result from the developed algorithm has shown its better solution in comparison with commercial scheduling software. Also emerging is an approach to shorten the convergence time of the algorithm by investigating the dependency of the algorithm's performance on tuning parameters. Applying  
Figure 10: Dynamic scheduling capacity of the developed solver.  
the developed ACO algorithm in this research can bring out various practical business values to companies. For example, complex MRO processes with various product routings can be optimally scheduled to improve productivity. The dynamic scheduling capability developed in this study can help production planners efficiently adjust the current product schedules to accommodate urgent work orders or additional work orders induced during the production process which frequently occurs in the MRO environment.  
There are several areas where the research can be carried on in the future. First, the algorithm can be improved to shorten the overall time required for the solution to converge. One of the possibilities is to modify the local and global pheromone updating strategies. Industrial applicability of the developed algorithm can be enhanced by considering more constraints such as sequence-dependent setup times, availabilities of materials and personnel. The integration of the developed algorithm to commercial scheduling software to harvest all advantages of the algorithm and the software is also under consideration with the support of middleware option [24]. Finally, alternative optimization algorithms such as bee colony for scheduling optimization will be also explored and developed.  
Conceptualization, L.V.T. and B.H.H.; methodology, L.V.T. and B.H.H; software, L.V.T.; validation, L.V.T, B.H.H. and H.A.; formal analysis, L.V.T. and B.H.H.; investigation, L.V.T.; resources, B.H.H. and H.A.; data curation, L.V.T.; writing--original draft preparation, L.V.T.; writing--review and editing, L.V.T, B.H.H. and H.A.; visualization, L.V.T. and B.H.H.; supervision, H.A.; project administration, B.H.H. and H.A.; funding acquisition, H.A.  
This research is supported by the Agency for Science, Technology and Research (A*STAR) under its Advanced Manufacturing & Engineering (AME) Industry Alignment Funding - Pre-positioning funding scheme (Project No: A1723a0035).  
The authors declare no conflict of interest.