Middle-mile logistics through the lens of goal-conditioned reinforcement learning
Onno Eberhard, Thibaut Cuvelier, Michal Valko, Bruno De Backer
TLDR
This paper applies goal-conditioned reinforcement learning and graph neural networks to optimize parcel routing in middle-mile logistics networks.
Key contributions
- Rephrases middle-mile logistics as a multi-object goal-conditioned Markov Decision Process (MDP).
- Combines Graph Neural Networks (GNNs) with model-free Reinforcement Learning for routing optimization.
- Extracts small feature graphs from the environment state to enhance learning efficiency.
Why it matters
Optimizing middle-mile logistics is crucial for supply chain efficiency. This work offers a new AI-driven framework, potentially leading to more efficient parcel routing and reduced operational costs in complex networks.
Original Abstract
Middle-mile logistics describes the problem of routing parcels through a network of hubs linked by trucks with finite capacity. We rephrase this as a multi-object goal-conditioned MDP. Our method combines graph neural networks with model-free RL, extracting small feature graphs from the environment state.
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