Reinforcement Learning-Based Ordering

This thesis investigates the applicability and performance of reinforcement learn- ing (RL) for inventory management of spare parts in a single-echelon setting. Traditional inventory control methods, such as (R,Q)-policies, typically decou- ple forecasting and ordering decisions, which may lead to suboptimal system- wide performance, particularly in environments characterized by intermittent and stochastic demand. To address these limitations, a reinforcement learning-based ordering model is developed and evaluated using both simulated and real-world data provided by Syncron AB. The proposed model is a Dueling Double Deep Q-Network (DDDQN) architecture incorporating enhancements such as prioritized expe- rience replay, experience buffer modification, and early stopping to improve training efficiency and stability. The performance of the RL-model is benchmarked against a standard peri- odic review (R,Q)-policy using backorder and holding cost. Results indicate that while RL demonstrates potential in capturing complex system dynamics and achieving competitive performance in certain scenarios, its effectiveness is highly dependent on data availability, demand characteristics, and computa- tional constraints. In particular, challenges related to intermittent demand, policy convergence, and generalization to unseen environments restrict consis- tent outperformance of traditional methods. The findings suggest that RL based approaches can complement existing inven- tory control methods but cannot yet fully replace them in practical applications. This highlights the importance of hybrid decision frameworks that leverage the strengths of both data-driven and analytical methods. The thesis concludes with recommendations for future development, including model scalability, improved data utilization, and more robust evaluation across varying demand patterns.