Deep reinforcement learning-based ordering mechanism for performance optimization in multi-echelon supply chains

The need for self-adaptive and intelligent supply chain systems is essential to meet the challenges of the current global markets. Despite the recent breakthroughs in artificial intelligence, literature still lacks the application of state-of-the-art methods to optimize the performance of supply chain ordering management problems. Thus, this paper proposes a relatively new Deep Reinforcement Learning-based Ordering Mechanism (DRLOM) for multi-echelon linear supply chain systems. Initially, the supply chain ordering management problem is formulated as an agent-based reinforcement learning model and, afterwards, solved using a recently developed policy-based algorithm called proximal policy optimization. The proposed approach (DRLOM) aids the assumed supply chain echelons, such as the Retailer, the Wholesaler, the Distributor and the Factory, to learn the optimal/near-optimal dynamic strategies for inventory ordering systems. The experimental results also validate that the proposed approach efficiently minimizes the system-wide total accumulated inventory costs under different problem instances than other ordering heuristics and evolutionary computation methods. Throughout this paper, benchmark findings from the literature are used to evaluate the performance of the proposed approach. Furthermore, limitations of the earlier works are addressed through this paper and contribute to the supply chain ordering management literature.