Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy order demands with maximum supply efficiency fully. Traditionally derived from financial portfolio management, the Black-Litterman (BL) model offers a new perspective for the TSSA scenario by balancing expected returns against insufficient supply risks. However, its application within TSSA is constrained by the reliance on manually constructed perspective matrices and spatio-temporal market dynamics, coupled with the absence of supervisory signals and data unreliability inherent to supplier information. To solve these limitations, we introduce the pioneering Deep Black-Litterman Model (DBLM), which innovatively adapts the BL model from financial roots to supply chain context. Leveraging the Spatio-Temporal Graph Neural Networks (STGNNS), DBLM automatically generates future perspective matrices for TSSA, by integrating spatio-temporal dependency. Moreover, a novel Spearman rank correlation distinctively supervises our approach to address the lack of supervisory signals, specifically designed to navigate through the complexities of supplier risks and interactions. This is further enhanced by a masking mechanism aimed at counteracting the biases from unreliable data, thereby improving the model's precision and reliability. Extensive experimentation on two datasets unequivocally demonstrates DBLM's enhanced performance in TSSA, setting new standards for the field. Our findings and methodology are made available for community access and further development.

翻译：时间序列供应商分配（TSSA）是一个复杂的NP难问题，旨在优化未来订单分配策略，以最高供应效率充分满足订单需求。源自金融投资组合管理的Black-Litterman（BL）模型为TSSA场景提供了新视角，通过平衡预期收益与供应不足风险实现优化。然而，该模型在TSSA中的应用受限于对人工构建视角矩阵和时空市场动态的依赖，同时面临供应商信息固有的监督信号缺失与数据不可靠性问题。为解决这些局限，我们提出开创性的深度Black-Litterman模型（DBLM），创新性地将BL模型从金融领域移植到供应链场景。通过利用时空图神经网络（STGNNS），DBLM通过整合时空依赖性自动生成TSSA的未来视角矩阵。此外，新颖的斯皮尔曼秩相关系数作为独特的监督机制，专门针对供应商风险与交互的复杂性设计，解决了监督信号缺失问题。我们进一步引入掩码机制以抵消不可靠数据造成的偏差，从而提升模型的精确性与可靠性。在两个数据集上的大量实验明确证明了DBLM在TSSA中的卓越性能，为该领域树立了新标杆。我们的研究成果与方法已向社区开放，以供访问和进一步开发。