An Intelligent Purchasing Decision Model for Railway Wagon Components Using a Markov Decision Process

Overcoming the inefficiency in the procurement of railway wagon components is a crucial step in promoting the modernization of railway enterprises' governance capabilities. This study formulates components purchasing for railway wagon as a two-stage Markov Decision Process (MDP) that jointly optimizes planned purchase quantities and supplier selection. Inputs to the MDP (demand signals and inventory) are treated as pre-processed operational forecasts from the enterprise data platform; the paper therefore focuses on the design of state/action spaces, reward construction, and the value-iteration solution for producing actionable purchasing decision. Using real business records from a large railway equipment group, simulation results demonstrate that the MDP-based decision reduces total purchasing cost, shortens purchasing lead time, and improves supplier quality metrics compared with legacy manual strategies.