Time Series Supplier Allocation via Deep Black-Litterman Model
Xinke Jiang, Wentao Zhang, Yuchen Fang, Xiaowei Gao, Hao Chen, Haoyu Zhang, Dingyi Zhuang, Jiayuan Luo
Abstract
As a typical problem of Spatiotemporal Resource Management, Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy the trade-off between demands and maximum supply. The Black-Litterman (BL) model, which comes from financial portfolio management, offers a new perspective for the TSSA by balancing expected returns against insufficient supply risks. However, the BL model is not only constrained by manually constructed perspective matrices and spatio-temporal market dynamics but also restricted by the absence of supervisory signals and unreliable supplier data. To solve these limitations, we introduce the pioneering Deep Black-Litterman Model for TSSA, which innovatively adapts the BL model from financial domain to supply chain context. Specifically, DBLM leverages Spatio-Temporal Graph Neural Networks (STGNNs) to capture spatio-temporal dependencies for automatically generating future perspective matrices. Moreover, a novel Spearman rank correlation is designed as our DBLM supervise signal to navigate complex risks and interactions of the supplier. Finally, DBLM further uses a masking mechanism to counteract the bias of unreliable data, thus improving precision and reliability. Extensive experiments on two datasets demonstrate significant improvements of DBLM on TSSA.
BibTeX
@article{Jiang_Zhang_Fang_Gao_Chen_Zhang_Zhuang_Luo_2025, title={Time Series Supplier Allocation via Deep Black-Litterman Model}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33292}, DOI={10.1609/aaai.v39i11.33292}, abstractNote={As a typical problem of Spatiotemporal Resource Management, Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy the trade-off between demands and maximum supply. The Black-Litterman (BL) model, which comes from financial portfolio management, offers a new perspective for the TSSA by balancing expected returns against insufficient supply risks. However, the BL model is not only constrained by manually constructed perspective matrices and spatio-temporal market dynamics but also restricted by the absence of supervisory signals and unreliable supplier data. To solve these limitations, we introduce the pioneering Deep Black-Litterman Model for TSSA, which innovatively adapts the BL model from financial domain to supply chain context. Specifically, DBLM leverages Spatio-Temporal Graph Neural Networks (STGNNs) to capture spatio-temporal dependencies for automatically generating future perspective matrices. Moreover, a novel Spearman rank correlation is designed as our DBLM supervise signal to navigate complex risks and interactions of the supplier. Finally, DBLM further uses a masking mechanism to counteract the bias of unreliable data, thus improving precision and reliability. Extensive experiments on two datasets demonstrate significant improvements of DBLM on TSSA.}, number={11}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Jiang, Xinke and Zhang, Wentao and Fang, Yuchen and Gao, Xiaowei and Chen, Hao and Zhang, Haoyu and Zhuang, Dingyi and Luo, Jiayuan}, year={2025}, month={Apr.}, pages={11870-11878} }