Abstract:

In bulk industrial logistics, order text typically contains implicit compatibility constraints that are challenging to identify accurately; disregarding destination spatial distribution during order splitting causes cross-region detours and exacerbates long-tail order accumulation. This paper proposes a collaborative scheduling framework integrating semantic awareness and uncertainty quantification for steel logistics. The semantic awareness module employs a domain-adapted sentence embedding model based on Bidirectional Encoder Representations from Transformers, latent relation graphs, and a mixture-of-experts routing module to model material compatibility, with industrial rules incorporated via large language model distillation for zero-shot inference on long-tail orders. The decision module adopts a variational hypernetwork that generates the mean and variance of network weights via variational inference, thus explicitly quantifying epistemic uncertainty and mitigating over-optimistic estimates for rare materials. Discrete routes and continuous split ratios are jointly optimized under a parameterized Markov decision process, with a differentiable physical constraint layer enforcing weight limits. On 1.7 million real industrial records, the framework achieved a competitive ratio of 0.8641 at approximately 100000 orders and a precision of 0.8942 in long-tail settings.

Key words: steel logistics, deep reinforcement learning, collaborative scheduling, semantic awareness, uncertainty quantification