Factorized Scheduling Principle: Learning Interpretable and Transferable Policies via Structured Additive Functions

Abstract:Scheduling problems arise from repeatedly selecting one item from a set of candidates based on their states. These problems often reduce to assigning priority scores and choosing the highest-ranked item. In this work, we propose a factorized scheduling principle (FSP) framework to learn interpretable and transferable scheduling rules. The FSP framework represents system states as condition distributions and decomposes a global scheduling principle into additive univariate and pairwise components with identifiability constraints. The scheduling principle enables the framework to maintain a simple priority-based structure during deployment. This principle is learned by using a policy-based objective combined with a temporal-difference signal defined on the condition distribution. Experiments on synthetic and realistic scheduling tasks demonstrate the FSP framework's strong performance, interpretability, and zero-shot generalization across different system scales.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.36578 [cs.LG]

(or arXiv:2609.36578v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.36578 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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