Mitigating a pyro-terror attack using fuel treatment

ABSTRACT

We study a security problem in which an adversary seeks to attack a landscape by setting a wildfire in a strategic location, whereas wildfire managers wish to mitigate the damage of the attack by implementing a fuel treatment in the landscape. We model the problem as a min–max Stackelberg game with the goal of identifying an optimal fuel treatment plan that minimizes the impact of a pyro-terror attack. As the adversary's problem is discrete, we use a decomposition algorithm suitable for integer bi-level programs. We test our model on three test landscape cases located in the Western United States. The results indicate that fuel treatment can effectively mitigate the effects of an attack: implementing fuel treatment on 2, 5, and 10% of the landscape, on average, reduces the damage caused by a pyro-terror attack by 14, 27, and 43%, respectively. The resulting fuel treatment plan is also effective in mitigating natural wildfires with randomly placed ignition points. The pyro-terrorism mitigation problem studied in this article is equivalent to the b-interdiction-covering problem where the intermediate nodes are subject to interdiction. It can also be interpreted as the problem of identifying the b-most-vital nodes in a one-to-all shortest path problem.

KEYWORDS:

• Critical infrastructure
• network interdiction
• Stackelberg game
• bilevel programming
• min–max problem
• maximal covering
• shortest path
• fuel treatment
• wildfire management
• pyro-terrorism

Additional information

Funding

This research was supported by the DHS through the National Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California (USC) under award number 2010-ST-061-RE0001. However, any opinions, findings, and conclusions or recommendations in this document are those of the authors and do not necessarily reflect views of the DHS, the USC, or CREATE.

Notes on contributors

Eghbal Rashidi

Eghbal Rashidi is currently a faculty member of the Operations Management and Information Systems Department, Leavey School of Business, at Santa Clara University. He received his Ph.D. in industrial and systems engineering from Mississippi State University in 2016. He then worked as a postdoctoral research fellow at Clemson University. His research interests include network optimization, stochastic optimization, multilevel optimization, and integer programming. In particular, he is motivated by applications of operations research in supply chain management, transportation, homeland security, and scheduling. His research has been published in journals such as European Journal of Operational Research, Transportation Research Part E, and International Journal of Advanced Manufacturing Technology. He is an active member of INFORMS, and he received the INFORMS Judith Liebman Award in 2015.

Hugh Richard Medal

Hugh Richard Medal is currently an assistant professor in the Department of Industrial and Systems Engineering at Mississippi State University. He holds Ph.D. and B.S. degrees in industrial engineering from the University of Arkansas and North Dakota State University, respectively. His research and teaching interests lie in optimization, with an emphasis on stochastic optimization, bi-level optimization, and network optimization. He has published articles on these topics in journals such as the European Journal of Operational Research, IISE Transactions, and Networks.  His 2016 article titled “Allocating Protection Resources to Facilities When the Effect of Protection is Uncertain” was featured in the Research Highlights section of IISE Magazine. His research has been funded by agencies such as the U.S. Army, the U.S. Joint Fire Science Program, and the U.S. Department of Homeland Security. He is an active member of INFORMS and currently serves as a board member for the Operations Research division of the Institute of Industrial and Systems Engineers.

Aaron Hoskins

Aaron B. Hoskins is an Aerospace Engineer at the U.S. Naval Research Laboratory. He received his Ph.D. in Industrial and Systems Engineering from Mississippi State in 2017. His research interests involve the application of stochastic programming and metaheuristics to complex domains such as forest fires and satellite orbits. His interdisciplinary work has been published in both the operations research field as well as the aerospace field.