Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search

2026-01-06

arXiv.org

AI Summary

This work addresses the performance degradation of existing deep reinforcement learning methods in online 3D bin packing under distribution shifts commonly encountered in real-world logistics scenarios. The authors formulate the packing problem with short-horizon lookahead information as a model predictive control (MPC) task and propose a Monte Carlo Tree Search (MCTS)-based optimization framework. Their approach innovatively incorporates a dynamic exploration prior to adaptively balance exploration and exploitation, alongside an auxiliary reward mechanism that penalizes long-term spatial wastage. Experimental results on real-world datasets demonstrate significant improvements over state-of-the-art methods: the proposed method achieves over 10% performance gain under distribution shift, an average 4% improvement in online deployment, and up to 8% enhancement in optimal scenarios.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Machine Learning: Online Learning & Bandits

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

Online 3D Bin Packing (3D-BP) with robotic arms is crucial for reducing transportation and labor costs in modern logistics. While Deep Reinforcement Learning (DRL) has shown strong performance, it often fails to adapt to real-world short-term distribution shifts, which arise as different batches of goods arrive sequentially, causing performance drops. We argue that the short-term lookahead information available in modern logistics systems is key to mitigating this issue, especially during distribution shifts. We formulate online 3D-BP with lookahead parcels as a Model Predictive Control (MPC) problem and adapt the Monte Carlo Tree Search (MCTS) framework to solve it. Our framework employs a dynamic exploration prior that automatically balances a learned RL policy and a robust random policy based on the lookahead characteristics. Additionally, we design an auxiliary reward to penalize long-term spatial waste from individual placements. Extensive experiments on real-world datasets show that our method consistently outperforms state-of-the-art baselines, achieving over 10\% gains under distributional shifts, 4\% average improvement in online deployment, and up to more than 8\% in the best case--demonstrating the effectiveness of our framework.

Problem

• Online 3D Bin Packing
• Distribution Shift
• Lookahead Parcels
• Robotic Logistics
• Short-term Adaptation

Innovation

• Monte Carlo Tree Search
• Model Predictive Control
• Lookahead Information
• Distribution Shift Robustness
• 3D Bin Packing