# Combinatorial Optimization and Machine Learning for Dynamic Inventory Routing

arXiv: https://arxiv.org/abs/2402.04463 · HTML: https://mlsanity.com/paper/2402.04463/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2024.

## Built on by (most cited first, 5 of 5)

- [Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems](https://mlsanity.com/paper/2407.17200/) (2024)
- [Combinatorial Optimization Augmented Machine Learning](https://mlsanity.com/paper/2601.10583/) (2026)
- [Optimization-Augmented Machine Learning for Vehicle Operations in Emergency Medical Services](https://mlsanity.com/paper/2503.11848/) (2025)
- [Constrained Reinforcement Learning for the Dynamic Inventory Routing Problem under Stochastic Supply and Demand](https://mlsanity.com/paper/2503.05276/) (2025)
- [Imitation Learning for Combinatorial Optimisation under Uncertainty](https://mlsanity.com/paper/2601.05383/) (2026)

## Abstract (arXiv)

We introduce a combinatorial optimization-enriched machine learning pipeline and a novel learning paradigm to solve inventory routing problems with stochastic demand and dynamic inventory updates. After each inventory update, our approach reduces replenishment and routing decisions to an optimal solution of a capacitated prize-collecting traveling salesman problem for which well-established algorithms exist. Discovering good prize parametrizations is non-trivial; therefore, we have developed a machine learning approach. We evaluate the performance of our pipeline in settings with steady-state and more complex demand patterns. Compared to previous works, the policy generated by our algorithm leads to significant cost savings, achieves lower inference time, and can even leverage contextual information.