Graph Neural Networks (GNNs) have gained traction across different domains
such as transportation, bio-informatics, language processing, and computer
vision. However, there is a noticeable absence of research on applying GNNs to
supply chain networks. Supply chain networks are inherently graph-like in
structure, making them prime candidates for applying GNN methodologies. This
opens up a world of possibilities for optimizing, predicting, and solving even
the most complex supply chain problems. A major setback in this approach lies
in the absence of real-world benchmark datasets to facilitate the research and
resolution of supply chain problems using GNNs. To address the issue, we
present a real-world benchmark dataset for temporal tasks, obtained from one of
the leading FMCG companies in Bangladesh, focusing on supply chain planning for
production purposes. The dataset includes temporal data as node features to
enable sales predictions, production planning, and the identification of
factory issues. By utilizing this dataset, researchers can employ GNNs to
address numerous supply chain problems, thereby advancing the field of supply
chain analytics and planning. Source: this https URL

应用图神经网络（GNN）到供应链网络的研究领域是相对缺失的，而本研究提出了一个用于时间任务的真实基准数据集，可以通过图神经网络来解决供应链规划等问题，从而推动供应链分析和规划领域的发展。

SupplyGraph：使用图神经网络进行供应链规划的基准数据集

SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph  Neural Networks

Transferring high-level knowledge from a source task to a target task is an
effective way to expedite reinforcement learning (RL). For example,
propositional logic and first-order logic have been used as representations of
such knowledge. We study the transfer of knowledge between tasks in which the
timing of the events matters. We call such tasks temporal tasks. We concretize
similarity between temporal tasks through a notion of logical transferability,
and develop a transfer learning approach between different yet similar temporal
tasks. We first propose an inference technique to extract metric interval
temporal logic (MITL) formulas in sequential disjunctive normal form from
labeled trajectories collected in RL of the two tasks. If logical
transferability is identified through this inference, we construct a timed
automaton for each sequential conjunctive subformula of the inferred MITL
formulas from both tasks. We perform RL on the extended state which includes
the locations and clock valuations of the timed automata for the source task.
We then establish mappings between the corresponding components (clocks,
locations, etc.) of the timed automata from the two tasks, and transfer the
extended Q-functions based on the established mappings. Finally, we perform RL
on the extended state for the target task, starting with the transferred
extended Q-functions. Our results in two case studies show, depending on how
similar the source task and the target task are, that the sampling efficiency
for the target task can be improved by up to one order of magnitude by
performing RL in the extended state space, and further improved by up to
another order of magnitude using the transferred extended Q-functions.

本文研究如何将逻辑知识从源任务迁移至时间任务中，并通过提出的传递学习方法对两个不同但相似时间任务之间的知识进行传递，取得了在扩展状态下执行强化学习时效率提高两个数量级的显著结果。