概率建模与鲁棒优化：基于人道主义物流案例的教程
Probabilistic Modeling versus Robust Optimization: A tutorial based on a humanitarian logistics use case
AI总结：
本文通过人道主义物流案例，对比概率建模与鲁棒优化在应对对抗性中断中的应用，提出结合两种方法的决策框架，优化调度时间并保护末端配送网络。
摘要：
本文通过人道主义物流案例，对比概率建模与鲁棒优化在应对对抗性中断中的应用，提出结合两种方法的决策框架，优化调度时间并保护末端配送网络。
英文摘要：
This tutorial contrasts probabilistic modeling and robust optimization to determine decisions in humanitarian logistics, specifically supply chains subject to adversarial (natural and human) disruptions. Natural disruptions induce dispatch of long-haul relief supply movement as storm forecasts evolve. A two-step workflow: (i) computes an initial pre-staging plan from the most likely forecast, and (ii) evaluates that fixed plan across plausible deviations in the eventual landfall location. In this way, dispatch decisions balance lead time and improved forecast information. For last-mile distribution, we propose deliveries when transportation networks must be protected against the worst case. We apply an iterative robust routing method that detects high-concentration links and increases their effective cost to promote route diversification. A case study based on Typhoon Noru (2022) shows how the combined approach identifies an optimal dispatch time and then protects last-mile delivery from difficult-to-predict network disruptions that could jeopardize the entire supply-chain operation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
