arXiv AI August 25, 2026 By Yi-Xiang Hu, Yuke Wang, Feng Wu, Zirui Huang, Shuli Zeng, Xiang-Yang Li

iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems

iScheduler is a reinforcement‑learning‑driven framework that tackles large‑scale Resource Investment Problems (RIP) by modeling them as a Markov decision process over decomposed subproblems and building schedules through sequential process selection. The approach speeds up optimization and allows efficient reconfiguration by reusing unchanged process schedules and only rescheduling affected processes. Using the new L‑RIPLIB benchmark, iScheduler achieves competitive resource costs while cutting time to feasibility by up to 43× compared to leading solver‑backed baselines.

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