Abstract As the demand for personalised customisation increases, manufacturing enterprises are continually enhancing their capabilities to meet diverse market requirements. This paper investigates a two-stage assembly flowshop scheduling problem, focusing on minimising total tardiness while accounting for resource flexibility and dynamic product arrivals using multi-agent deep reinforcement learning (MADRL).