## 5 Conclusions  
We presented the Ferodata AI engine, an AI/ML optimization module, developed by our team, that automatically assigns drivers to freight transportation orders based on the data about drivers, workplaces, driving/service times, and circulation history. The model proposed by us is the first of its kind to be published and obtained excellent performance metrics on the train set and very good and encouraging performance metrics on the test set. The presentation of the Ferodata AI engine and the publication of this research paper for the rail freight transportation industry can have several important implications and benefits for the industry as a whole. One of the key benefits of an AI/ML optimization module like the Ferodata AI engine is the potential for significant efficiency improvements. By automatically assigning drivers to freight transportation orders based on various data points, idle time can be reduced and overall resource allocation can be improved. This leads to cost savings for transportation companies and potentially lower prices for customers. The ability to assign drivers to orders with precision can lead to better service quality. Timely deliveries and reduced delays can enhance customer satisfaction, making rail freight transportation a more attractive option for businesses that rely on it. AI-driven optimization can help rail freight companies make the most of their resources. It can balance workloads, prevent the overworking of certain drivers, and ensure that resources are used efficiently across the network. Optimized driver assignments can also lead to safety improvements. By managing driver schedules and driving times more effectively, it is possible to reduce fatigue-related accidents and enhance overall safety within the industry. By optimizing driver schedules and ensuring compliance with regulations regarding working hours and rest breaks, the Ferodata AI engine can help rail freight companies maintain regulatory compliance and avoid fines or penalties. After rigorous testing and validation on external and larger datasets, the automated optimization of locomotive driver assignments could bring operational efficiency, cost savings, regulatory compliance, and improved safety to scheduled rail freight transports. If the AI/ML optimization module proves successful, it may encourage other rail freight companies to adopt similar technologies. This can lead to an industry-wide shift towards greater efficiency and improved services.  
Conceptualization, A.B., O.G. and I.V.P.; methodology, A.B. and I.V.P.; software, I.V.P.; validation, A.B. and O.G.; formal analysis, A.B. and I.V.P.; investigation, A.B. and O.G.; resources, A.B.; data curation, I.V.P.; writing--original draft preparation, A.B., O.G. and I.V.P.; writing--review and editing, I.V.P. and A.B.; supervision, A.B. and I.V.P.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Innovation Norway through the programme SME Growth Romania--Priority ICT, Project 2020/548467.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
[PERSON] is employee of GREENSOFT S.R.L., who provided funding and technical support for the work. The funder had no role in the design of the study; in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.