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

arXiv: https://arxiv.org/abs/2112.08736 · HTML: https://mlsanity.com/paper/2112.08736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Go-Explore: a New Approach for Hard-Exploration Problems](https://mlsanity.com/paper/1901.10995/) (2019)
- [AI Safety Gridworlds](https://mlsanity.com/paper/1711.09883/) (2017)
- [Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows](https://mlsanity.com/paper/2102.12088/) (2021)
- [Developing an OpenAI Gym-compatible framework and simulation environment for testing Deep Reinforcement Learning agents solving the Ambulance Location Problem](https://mlsanity.com/paper/2101.04434/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce](https://mlsanity.com/paper/2311.16171/) (2023)

## Abstract (arXiv)

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.