Data-Driven Block Replacement Scheduling: Multi-Armed Bandit Based Nonparametric Cost Optimization
Score
75将多臂老虎机与块替换维护问题结合，并利用嵌套观测特性创新，理论结果新颖。
理论推导严谨，算法设计精巧，实验设计完整，写作清晰。
对工业维护决策有直接指导意义，但算法假设完全同步替换，实际需考虑复杂失效模式。
论文为2025年7月最新上传，反映当前数据驱动维护优化前沿。
arXiv预印本，但作者来自顶尖运筹学/机器学习团队（推测），算法可复现。
Base 83 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
Solid theoretical contributions (matching lower bounds, nonparametric consistency), clever algorithm design (exploiting nested observations), thorough experiments, yet the application domain is somewhat specific.
AI commentary
「This paper cleverly integrates maintenance scheduling with multi-armed bandits, and the exploitation of nested observations is particularly ingenious. For those working in industrial AI or operations research optimization, it offers an elegant and practical nonparametric framework; for theoreticians, the regret bound matching and MDP analysis are equally worth careful study.」
Quick overview
This paper addresses the block replacement scheduling problem for N independent identical machines with unknown lifetime distributions. By formulating the interval selection as a stochastic multi-armed bandit, the authors propose Hoeffding- and Bernstein-based lower-confidence-bound algorithms achieving O(K log T) regret, matching the Lai–Robbins lower bound. Exploiting the nested observation property unique to block replacement, correlated variants attain O((K-k*) log T) regret and require only O(1) direct pulls of suboptimal arms k < k*. A complementary Kaplan–Meier renewal algorithm nonparametrically estimates the lifetime distribution from censored data, ensuring almost-sure policy consistency. Numerical experiments confirm theoretical ordering and reveal structural cost gaps between optimal block and age-dependent replacement.
- Formulates block replacement scheduling as a stochastic multi-armed bandit and designs two LCB algorithms achieving optimal regret bounds.
- Leverages nested observation property to develop correlated variants with reduced exploration cost for suboptimal arms.
- Introduces Kaplan–Meier renewal algorithm for nonparametric distribution estimation from censored data, ensuring policy consistency.
- Analyzes two average-cost MDPs, proving optimality of block replacement within its policy class and revealing cost gaps with age-dependent replacement.
Full text
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