2026-06-23 ヒューストン大学（UH）
A new mathematical model developed at UH shows that targeted investments in a small number of critical assets can significantly improve infrastructure resilience and disaster recovery.
＜関連情報＞
- https://www.uh.edu/news-events/stories/2026/june/06232026-lim-disaster-priorities-model.php
- https://www.sciencedirect.com/science/article/abs/pii/S0360835226003219
インフラのレジリエンスに関する確率制約付き最適化：公益事業主導の予算配分フレームワーク Chance-constrained optimization of infrastructure resilience: A utility-driven budget allocation framework
Tugce Uslu Aktas, Gino J. Lim, Jian Shi
Computers & Industrial Engineering Available online: 23 May 2026
DOI:https://doi.org/10.1016/j.cie.2026.112120
Highlights
- A chance-constrained model is developed to enhance system resilience.
- The framework is demonstrated to be applicable under various distributional assumptions.
- A successive convex approximation is applied to non-convex utility functions.
- Implementation is illustrated via real-world power and transport cases.
- An efficient budget allocation plan to maximize resilience is obtained.
Abstract
This paper presents a novel chance-constrained model to enhance infrastructure resilience in a stochastic environment under budget constraints. Our goal is to maximize resilience by accounting for the system’s robustness and recovery capability, both of which are essential indicators of a resilient system. The uncertainty in functional degradation and recovery time of components is considered due to the uncertain intensity of adverse occurrences. The framework accommodates multiple distributional assumptions, including normal and uniform distributions, and integrates both linear and nonlinear utility functions to determine optimal trade-offs between reducing functional degradation and improving recovery. In addition, to efficiently solve the nonconvex utility formulation, a successive convex approximation algorithm is implemented, which significantly improves computational performance. The model is validated through three case studies (an illustrative system, a power transmission network, and a transportation network), demonstrating its applicability across different infrastructures. The results highlight the framework’s ability to generate efficient budget allocation plans that maximize resilience while accounting for uncertainty in extreme events.
