  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.
