Scheduling precedence-constrained tasks under shared renewable resources is central to modern computing platforms. The Resource Investment Problem (RIP) models this setting by minimizing the cost of provisioned renewable resources under precedence and timing constraints. Exact mixed-integer programming and constraint programming become impractically slow on large instances, and dynamic updates require schedule revisions under tight latency budgets. We present iScheduler, a reinforcement-learning-driven iterative scheduling framework that formulates RIP solving as a Markov decision process over decomposed subproblems and constructs schedules through sequential process selection. The framework accelerates optimization and supports reconfiguration by reusing unchanged process schedules and rescheduling only affected processes. We also release L-RIPLIB, an industrial-scale benchmark derived from cloud-platform workloads with 1,000 instances of 2,500-10,000 tasks. Experiments show that iScheduler attains competitive resource costs while reducing time to feasibility by up to 43× against strong commercial baselines.
@article{arxiv.2602.06064,
title = {iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems},
author = {Yi-Xiang Hu and Yuke Wang and Feng Wu and Zirui Huang and Shuli Zeng and Xiang-Yang Li},
journal= {arXiv preprint arXiv:2602.06064},
year = {2026}
}