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.     

攻击主动防御飞行器的微分对策制导律

基于微分对策理论设计了躲避护卫弹的同时攻击飞行器的制导律。根据传统的性能指标推导了该场景的微分对策制导律,并且根据权重系数的取值定义了三种制导律:最优追赶制导律、逃脱-追赶制导律和复合制导律。最优追赶制导律容易被护卫弹拦截,逃脱-追赶制导律容易造成导弹和飞行器的零控脱靶量急剧增大而使得攻击失败,复合制导律很难选择合适的权重系数。针对以上不足,提出了两种改进的制导律,并对该两种制导律的适用情况进行了分析。通过非线性模型仿真,验证了这两种方法的可行性。该两种制导律目的性强,攻击导弹可以躲避护卫弹进而攻击飞行器。     

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