Deep reinforcement learning-based approach for dynamic routing in quick-commerce e-fulfilment systems

This study investigates the deep reinforcement learning (DRL)-based approach to manage dynamic same-day delivery orders in e-fulfilment systems connected to physical and internet logistics network. To enable real-time response to ad-hoc changes, a DRL-based approach which employs the actor–critic mechanism is proposed to generate dynamic vehicle routing solutions for q-commerce order additions and cancellations. Computational experiments were conducted to compare the performance of OR-Tools (ORT) and the proposed method in static and dynamic environments. The experimental results showed that the proposed deep reinforcement learning q-fulfilment routing optimizer (DRLQRO) offered similar performance to ORT in a static environment with higher vehicle capacity, while it outperformed ORT in a dynamic environment with lower vehicle capacity, achieving cost savings of 12.3–19.55%. The DRLQRO features adaptability and robustness, making them suitable for e-fulfilment systems in the digital twin era.