# Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations

arXiv: https://arxiv.org/abs/2408.01656 · HTML: https://mlsanity.com/paper/2408.01656/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 56 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 4 of 4)

- [Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning](https://mlsanity.com/paper/2006.09507/) (2020)
- [Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems](https://mlsanity.com/paper/2310.14157/) (2023)
- [Policies for the Dynamic Traveling Maintainer Problem with Alerts](https://mlsanity.com/paper/2105.15119/) (2021)
- [Deep Reinforcement Learning for Picker Routing Problem in Warehousing](https://mlsanity.com/paper/2402.03525/) (2024)

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

- [Drive, Pack, Fly: The Travelling Thief Problem with Drone](https://mlsanity.com/paper/2608.16435/) (2026)

## Abstract (arXiv)

Order picking is a pivotal operation in warehouses that directly impacts overall efficiency and profitability. This study addresses the dynamic order picking problem, a significant concern in modern warehouse management, where real-time adaptation to fluctuating order arrivals and efficient picker routing are crucial. Traditional methods, which often depend on static optimization algorithms designed around fixed order sets for the picker routing, fall short in addressing the challenges of this dynamic environment. To overcome these challenges, we propose a Deep Reinforcement Learning (DRL) framework tailored for single-block warehouses equipped with an autonomous picking device. By dynamically optimizing picker routes, our approach significantly reduces order throughput times and unfulfilled orders, particularly under high order arrival rates. We benchmark our DRL model against established algorithms, utilizing instances generated based on standard practices in the order picking literature. Experimental results demonstrate the superiority of our DRL model over benchmark algorithms. For example, at a high order arrival rate of 0.09 (i.e., 9 orders per 100 units of time on average), our approach achieves an order fulfillment rate of approximately 98%, compared to the 82% fulfillment rate observed with benchmarking algorithms. We further investigate the integration of a hyperparameter in the reward function that allows for flexible balancing between distance traveled and order completion time. Finally, we demonstrate the robustness of our DRL model on out-of-sample test instances.