The incorporation of artificial intelligence (AI) and robust optimization methods for the planning and design of relief logistics networks under relief demand–supply uncertainty appears promising for intelligent disaster management (IDM). This research proposes a data-driven hybrid scenario-based robust (SBR) method for a mixed integer second-order cone programming (MISOCP) model that integrates machine learning with a hybrid robust optimization approach to address the above issue. A machine learning technique is utilized to cluster the casualties based on location coordinates and injury severity score. Moreover, the hybrid SBR optimization method and robust optimization based on the uncertainty sets technique are utilized to cope with uncertain parameters such as the probability of facility disruption, the number of wounded individuals, transportation time, and relief demand. Additionally, the epsilon-constraint technique is applied to seek the solution for the bi-objective model. Focusing on a real case (the Kermanshah disaster), our analytical results have demonstrated not only the validity but also the relative merits of the proposed methodology against typical stochastic and robust optimization approaches. Besides, the proposed method shows all casualties can be efficiently transported to receive medical services at a fair cost, which is crucial for disaster management.

中文翻译：

---

一种基于数据驱动的混合场景鲁棒优化方法，用于救援物流网络设计


在救灾供需不确定性下，将人工智能 （AI） 和稳健的优化方法用于规划和设计救灾物流网络，这似乎对智能灾害管理 （IDM） 很有希望。本研究为混合整数二阶锥规划 （MISOCP） 模型提出了一种数据驱动的基于场景的混合鲁棒 （SBR） 方法，该方法将机器学习与混合鲁棒优化方法相结合，以解决上述问题。利用机器学习技术根据位置坐标和受伤严重程度评分对伤亡人数进行聚类。此外，利用混合 SBR 优化方法和基于不确定性集技术的稳健优化来应对不确定参数，例如设施中断的概率、受伤人数、运输时间和救援需求。此外，还应用了 epsilon-constraint 技术来寻找双目标模型的解。专注于一个真实案例（Kermanshah 灾难），我们的分析结果不仅证明了所提出的方法的有效性，而且证明了与典型的随机和稳健优化方法相比的相对优点。此外，所提出的方法表明，所有伤员都可以以合理的成本有效地运送到接受医疗服务，这对于灾害管理至关重要。