# Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search

arXiv: https://arxiv.org/abs/2506.02746 · HTML: https://mlsanity.com/paper/2506.02746/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 3 Jun 2025.

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

- [Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning](https://mlsanity.com/paper/2211.00759/) (2022)
- [Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding](https://mlsanity.com/paper/2502.10233/) (2025)
- [Active repositioning of storage units in Robotic Mobile Fulfillment Systems](https://mlsanity.com/paper/1801.04105/) (2018)
- [Deterministic Pod Repositioning Problem in Robotic Mobile Fulfillment Systems](https://mlsanity.com/paper/1810.05514/) (2018)
- [E-commerce warehousing: learning a storage policy](https://mlsanity.com/paper/2101.08828/) (2021)

## Abstract (arXiv)

The Pod Repositioning Problem (PRP) in Robotic Mobile Fulfillment Systems (RMFS) involves selecting optimal storage locations for pods returning from pick stations. This work presents an improved solution method that integrates Adaptive Large Neighborhood Search (ALNS) with Deep Reinforcement Learning (DRL). A DRL agent dynamically selects destroy and repair operators and adjusts key parameters such as destruction degree and acceptance thresholds during the search. Specialized heuristics for both operators are designed to reflect PRP-specific characteristics, including pod usage frequency and movement costs. Computational results show that this DRL-guided ALNS outperforms traditional approaches such as cheapest-place, fixed-place, binary integer programming, and static heuristics. The method demonstrates strong solution quality and illustrating the benefit of learning-driven control within combinatorial optimization for warehouse systems.