Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce
In the fast-paced world of e-commerce, minimizing product delivery costs—often referred to as the cost-to-serve (C2S) is a significant challenge. This project introduced an integrated algorithmic framework designed to tackle this issue by efficiently managing the high volume of spatio-temporally diverse orders that must be fulfilled from multiple warehouses using a fleet of vehicles. The project focuses on two critical decision-making levels: (i) selecting the optimal fulfillment node for each order, with the option of deferring orders to future time slots, and (ii) routing vehicles that carry multiple orders from the same warehouse. The proposed solution leverages graph neural networks and reinforcement learning to train agents for both node selection and vehicle routing, ensuring that real-world constraints such as warehouse inventory capacity, vehicle characteristics, and customer delivery time windows are fully considered.
The complexity of this problem is amplified by the interdependence of fulfillment node selection and vehicle routing, both of which influence the final outcomes. Our experiments demonstrate that this AI-driven approach significantly outperforms traditional heuristic methods, providing a more efficient and scalable solution to e-commerce delivery logistics.
Dec 5, 2023
