Applying metaheuristic methods for staffing in railway depots

Abstract Ensuring the smooth operation of a railway transport node, such as the maintenance depot, is highly demanding due to the complexity of the system. Many components are closely interconnected, and numerous constraints must be considered. Moreover, all workflow processes are frequently disrupted by a significant number of unexpected interruptions, such as delays, technical failures, or personnel shortages. These challenges highlight the necessity of optimizing both operational planning and real-time control. The field of operational research provides a wide range of optimization methods to improve efficiency in the management of railway transport nodes. However, exact mathematical models often prove unsuitable due to their computational complexity and the time required to obtain solutions, especially when dealing with large-scale operational problems. Instead, heuristic and metaheuristic approaches have emerged as practical alternatives, offering a balance between solution quality and computational time. One of the important aspects of depot management is the assignment of staff, particularly train drivers, to various tasks and activities. Efficient staff allocation is essential to minimize delays, optimize workload distribution, and reduce operational costs. In our research, we focus on two heuristic methods – SA and genetic algorithms – as potential tools for solving the driver assignment problem. These methods have demonstrated their effectiveness in achieving near-optimal solutions within a reasonable time frame, making them well-suited for real-world applications in railway maintenance depots.