Critical infrastructures face demanding challenges due to natural and human-generated threats, such as pandemics, workforce shortages or cyber-attacks, which might severely compromise service quality. To improve system resilience, decision-makers would need intelligent tools for quick and efficient resource allocation. This article explores an agent-based simulation model that intends to capture a part of the complexity of critical infrastructure systems, particularly considering the interdependencies of healthcare systems with information and telecommunication systems. Such a model enables to implement a simulation-based optimization approach in which the exposure of critical systems to risks is evaluated, while comparing the mitigation effects of multiple tactical and strategical decision alternatives to enhance their resilience. The proposed model is designed to be parameterizable, to enable adapting it to risk scenarios with different severity, and it facilitates the compilation of relevant performance indicators enabling monitoring at both agent level and system level. To validate the agent-based model, a literature-supported methodology has been used to perform cross-validation, sensitivity analysis and test the usefulness of the proposed model through a use case. The use case analyzes the impact of a concurrent pandemic and a cyber-attack on a hospital and compares different resiliency-enhancing countermeasures using contingency tables. Overall, the use case illustrates the feasibility and versatility of the proposed approach to enhance resiliency.

翻译：关键基础设施面临着自然和人为威胁带来的严峻挑战，例如流行病、劳动力短缺或网络攻击，这些威胁可能严重损害服务质量。为提升系统韧性，决策者需要智能工具来实现快速高效的资源分配。本文探讨了一种基于智能体的仿真模型，旨在捕捉关键基础设施系统复杂性的一个侧面，特别关注医疗系统与信息通信系统之间的相互依存关系。该模型能够实现基于仿真的优化方法，在评估关键系统风险暴露程度的同时，比较多种战术与战略决策方案对提升系统韧性的缓解效果。所提出的模型采用参数化设计，可适配不同严重程度的风险场景，并支持编制相关性能指标，实现智能体层面与系统层面的双重监测。为验证该智能体模型，研究采用文献支持的方法进行交叉验证和敏感性分析，并通过用例测试模型的有效性。该用例分析了并发流行病与网络攻击对医院的影响，并利用列联表比较了不同韧性提升对策的效果。总体而言，该用例证明了所提方法在增强韧性方面的可行性与普适性。