Minimization of Material Waste Through Maintenance Interval Optimization in Transport Systems

The optimization of maintenance intervals is crucial for enhancing efficiency, sustainability, and cost-effectiveness in transport operations. This paper presents a method for optimizing maintenance intervals for vehicles in various modes of transport, focusing on minimizing downtime due to repairs and maintenance. By integrating advanced technologies like artificial intelligence (AI) and the Internet of Things (IoT), maintenance intervals are dynamically adjusted using real-time data, resulting in better resource utilization and reduced operational costs. The key findings of this research indicate significant reductions in downtime and maintenance costs, leading to improved efficiency and sustainability across transport modes. Although the case study is based on railway vehicles, the approach is applicable to road, maritime, and air transport as well. By leveraging optimization algorithms, such as machine learning, this solution predicts optimal maintenance timing, thereby reducing resource consumption and improving operational efficiency. The case study on pantograph maintenance demonstrates significant financial savings and reduced waste. This research highlights the benefits of maintenance optimization for sustainability and efficiency across the entire transport sector.