Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce
This addresses cost reduction in e-commerce logistics for businesses, but it is incremental as it builds on existing methods in a specific domain.
The paper tackles the problem of minimizing cost-to-serve for multi-node multi-product order fulfillment in e-commerce by comparing baselines like heuristics and mixed-integer linear programming, showing that a reinforcement learning algorithm is competitive with these policies and has potential for efficient real-world scale-up.
We describe a novel decision-making problem developed in response to the demands of retail electronic commerce (e-commerce). While working with logistics and retail industry business collaborators, we found that the cost of delivery of products from the most opportune node in the supply chain (a quantity called the cost-to-serve or CTS) is a key challenge. The large scale, high stochasticity, and large geographical spread of e-commerce supply chains make this setting ideal for a carefully designed data-driven decision-making algorithm. In this preliminary work, we focus on the specific subproblem of delivering multiple products in arbitrary quantities from any warehouse to multiple customers in each time period. We compare the relative performance and computational efficiency of several baselines, including heuristics and mixed-integer linear programming. We show that a reinforcement learning based algorithm is competitive with these policies, with the potential of efficient scale-up in the real world.