用强化学习优化锂矿供应链，应对地质不确定性挑战
Managing Geological Uncertainty in Critical Mineral Supply Chains: A POMDP Approach with Application to U.S. Lithium Resources
- 引入部分可观测马尔可夫决策过程（POMDP）建模地质不确定性
- 在美国内陆锂资源案例中，性能优于传统方法，尤其初始储量估算不准时
- 为政策制定者提供本土开发与国际采购的量化平衡方案
全球向可再生能源和电动汽车转型正推动关键矿物需求进入前所未有的高峰期。这一转型带来矿物资源开发的独特挑战，尤其是地质不确定性这一核心特征，而传统供应链优化方法未能充分应对。为此，本文提出一种新型的偏可观测马尔可夫决策过程（POMDP）应用，用于在动态地质不确定性下优化关键矿物采购策略。通过美国锂资源供应链的案例研究，结果表明，在初始储量估计不准确的情况下，基于POMDP的策略表现显著优于传统方法。该框架为平衡本土资源开发与国际供应多元化提供了量化依据，为政策制定者在关键矿物供应链中的战略决策提供了系统性支持。
原文摘要 · Abstract (English)
The world is entering an unprecedented period of critical mineral demand, driven by the global transition to renewable energy technologies and electric vehicles. This transition presents unique challenges in mineral resource development, particularly due to geological uncertainty-a key characteristic that traditional supply chain optimization approaches do not adequately address. To tackle this challenge, we propose a novel application of Partially Observable Markov Decision Processes (POMDPs) that optimizes critical mineral sourcing decisions while explicitly accounting for the dynamic nature of geological uncertainty. Through a case study of the U.S. lithium supply chain, we demonstrate that POMDP-based policies achieve superior outcomes compared to traditional approaches, especially when initial reserve estimates are imperfect. Our framework provides quantitative insights for balancing domestic resource development with international supply diversification, offering policymakers a systematic approach to strategic decision-making in critical mineral supply chains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。