A Unified Framework for Multi-Stage Decision Optimization with Deep Reinforcement Learning and Foundation Models

In today’s rapidly information-sharing and highly coordinated industries, multi-stage management is pivotal for enhancing adaptability and optimizing profitability in dynamic and uncertain business environments. However, conventional heuristic approaches often struggle to capture dynamic inter-dependencies and adjust to real-time fluctuations. To address these limitations, we first employ Deep Reinforcement Learning (DRL) to optimize individual management stages, demonstrating its effectiveness over heuristics in isolated scenarios. Building on this foundation, we propose a novel Foundation Model for Management Decision-Making (FMMD), a transformer-based foundation model integrated with DRL, thereby enabling unified cross-domain decision-making across production, inventory management, dynamic pricing, and recommendation. Experimental evaluations show that our method substantially outperforms existing approaches, underscoring its transformative potential. Through an end-to-end generative mechanism, FMMD holistically coordinates actions among interconnected domains, significantly boosting efficiency and adaptability. By unifying multiple management stages under an AI-driven framework, FMMD lays the foundation for the construction of a large management model capable of accommodating massive data, actions, and scales. Our framework highlights a new paradigm in business management and paves the way for future research in automation science and decision-making processes.