An attacker‐defender model for analyzing the vulnerability of initial attack in wildfire suppression

Wildfire managers use initial attack (IA) to control wildfires before they grow large and become difficult to suppress. Although the majority of wildfire incidents are contained by IA, the small percentage of fires that escape IA causes most of the damage. Therefore, planning a successful IA is very important. In this article, we study the vulnerability of IA in wildfire suppression using an attacker‐defender Stackelberg model. The attacker's objective is to coordinate the simultaneous ignition of fires at various points in a landscape to maximize the number of fires that cannot be contained by IA. The defender's objective is to optimally dispatch suppression resources from multiple fire stations located across the landscape to minimize the number of wildfires not contained by IA. We use a decomposition algorithm to solve the model and apply the model on a test case landscape. We also investigate the impact of delay in the response, the fire growth rate, the amount of suppression resources, and the locations of fire stations on the success of IA.     

Allocating capacity in parallel queues to improve their resilience to deliberate attack

We develop models that lend insight into how to design systems that enjoy economies of scale in their operating costs, when those systems will subsequently face disruptions from accidents, acts of nature, or an intentional attack from a well‐informed attacker. The systems are modeled as parallel M/M/1 queues, and the key question is how to allocate service capacity among the queues to make the system resilient to worst‐case disruptions. We formulate this problem as a three‐level sequential game of perfect information between a defender and a hypothetical attacker. The optimal allocation of service capacity to queues depends on the type of attack one is facing. We distinguish between deterministic incremental attacks, where some, but not all, of the capacity of each attacked queue is knocked out, and zero‐one random‐outcome (ZORO) attacks, where the outcome is random and either all capacity at an attacked queue is knocked out or none is. There are differences in the way one should design systems in the face of incremental or ZORO attacks. For incremental attacks it is best to concentrate capacity. For ZORO attacks the optimal allocation is more complex, typically, but not always, involving spreading the service capacity out somewhat among the servers. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

对地攻击机靶场作战效能的基本问题

讨论了对地攻击靶场作战效能分析过程中所涉及到的主要问题及靶场作战效能指标的计算原理,简要介绍了一个实用的对地攻击机靶场作战效能分析软件的主要功能.     

攻击机靶场效能优化:概念、方法与基本问题

系统阐述了攻击机靶场攻击、靶场效能以及靶场效能优化等基本概念,在此基础上,分析了攻击机靶场效能优化的基本任务和基本问题,并给出了形式化描述。     

攻击机首攻概率模型

攻击机在首次进入攻击时完成作战任务的概率(首攻概率)是评估空军武器进攻装备作战效能的基础。从无对抗情况下攻击机的作战过程入手,分析建立了攻击机机载空地武器可攻击区的数学模型,在此基础上建立了无对抗情况下攻击机的首攻概率模型,并对所建立的概率模型进行了有效性分析。