# Minimization of Material Waste Through Maintenance Interval Optimization in Transport Systems
## 5 Discussion  
The solution presented in this publication forms the foundation for:  
* Optimal scheduling of vehicle operations (locomotives, trucks, ships, and, if possible, individual vehicle units). The vehicle scheduling system allows for creating an optimal work plan for vehicles on a given day, as well as planning transitions between different routes over weekly, monthly, or other defined periods;
* Optimal scheduling of employee shifts and providing such tools to transport operators. The shift optimization tool will enable creating an optimal set of tasks for employees for a specific accounting period;
* Designing and continuously updating the maintenance and repair plan for vehicles, enabling oversight of proper fleet management within the company and providing such tools to transport operators. The algorithms should utilize available data while also predicting operational scenarios for vehicles in the company, with the ability to automatically adjust calculations over time;
* Simulation and optimization of transport routes based on theoretical route parameters (road class, rail class, line profile, maritime parameters, etc.) and vehicle characteristics (gross weight, maximum vehicle speed, % braking mass, etc.). Additionally, the tool should simulate the schedule for a given line or route operated by the carrier and make this tool available to transport operators. However, it is worthwhile to more thoroughly discuss the impact of maintenance optimization on all sectors of transport, not just rail transport. Implementing optimized maintenance intervals could bring significant benefits in the road, air, maritime, as well as rail sectors, by reducing operational costs and minimizing the risk of unplanned downtime. Technologies supporting optimization, such as artificial intelligence, the Internet of Things (IoT), and machine learning, can significantly enhance the efficiency of the proposed solutions. For example, combining predictive maintenance models with real-time data obtained from sensors mounted on vehicles would enable dynamic analysis of the vehicle's technical condition, potentially speeding up responses to emergencies and reducing downtime. This approach could be applied in road transport to monitor the condition of trucks, in aviation to assess engine health, and in maritime transport to analyze the status of propulsion units. The environmental impact of the proposed solution also deserves a broader discussion. By optimizing maintenance schedules, we can significantly reduce the consumption of spare materials and the amount of waste generated during servicing, which directly contributes to reducing CO\({}_{2}\) emissions. Comparing the current maintenance methodology with the proposed optimization, we can observe significant ecological benefits that could also be translated to all branches of transport. Based on the AEA report [27], CO\({}_{2}\) emissions for rail vehicles are approximately 49 g/km [28]. Due to the reduction in unnecessary services and the distance required to reach these services--around 92,000 km--the estimated total CO\({}_{2}\) emissions amount to approximately 4,508,000 g over the entire working period (almost 25 years). Therefore, the annual emissions are estimated to be about 0.181915 tonnes of CO\({}_{2}\). Since the developed model holds particular significance for railway transport, where maintenance requirements are more structured and overlapping, the need for optimization is greater compared to maritime, air, and road transport, where these requirements do not coincide as frequently. The proposed algorithm can also be applied to other branches of transport; however, in those cases, the key components that require servicing should be analyzed individually. After such a modification, the algorithm could be used to determine synchronized maintenance schedules for multiple components, reducing service costs and minimizing overall service time. Preparing the modifications to use this algorithm in different types of transport is the future area of our research.