Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce

TL;DR

This paper introduces a reinforcement learning approach to optimize cost-to-serve in complex e-commerce supply chains, addressing delivery from multiple warehouses to many customers, and demonstrates its competitive performance against traditional methods.

Contribution

It presents a novel RL-based decision-making algorithm for multi-node, multi-product order fulfillment in e-commerce, showing promise for scalable, efficient logistics optimization.

Findings

RL algorithm is competitive with heuristics and MILP methods

Demonstrates potential for real-world scalability

Addresses large-scale, stochastic supply chain challenges

Abstract

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.