A comprehensive deep reinforcement learning concept model for omnichannel fulfillment

The rapid evolution of omnichannel retail demands innovative fulfillment solutions to meet dynamic customer expectations. This study presents a conceptual fulfillment model leveraging Deep Reinforcement Learning (DRL) to optimize decisions across inventory management, order allocation, and delivery logistics. Simulations of a medium-sized retailer’s operations over a year reveal the DRL model’s adaptability, achieving a 12.6% higher profit compared to traditional heuristics. While results underline DRL’s potential in dynamic environments, further empirical validation with real-world data is essential to ensure robustness and scalability. This research advances the integration of AI in supply chain.