ENHANCEMENT OF VENDOR-MANAGED INVENTORY PLANNING THROUGH DEEP REINFORCEMENT LEARNING

We explore the application of Twin Delayed Deep Deterministic Policy Gradient (TD3), a Deep Reinforcement Learning (DRL) algorithm, for optimizing Vendor-Managed Inventory (VMI) systems in the semiconductor industry. We introduce a novel multi-scenario DRL algorithm with a continuous action space, designed to effectively manage diverse product/customer combinations, thereby improving VMI performance. We evaluate our algorithm’s efficacy on three distinct products as well as 100 product/customer combinations for the multi-scenario approach. A sensitivity analysis examines the effects of varying shipment penalties on the percentage of no violations (PNV) and shipments. Our findings indicate that our DRL-based VMI model significantly surpasses existing policies used in the semiconductor industry by five percentage points.