Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search
This work addresses a specific optimization problem in warehouse automation, representing an incremental improvement through hybrid methods.
The paper tackles the Pod Repositioning Problem in robotic warehouse systems by integrating Deep Reinforcement Learning with Adaptive Large Neighborhood Search to dynamically control search operators and parameters, resulting in improved solution quality over traditional methods like cheapest-place and binary integer programming.
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.