Optimization Approaches To Protect Transportation Infrastructure Against Strategic and Random Disruptions

Past and recent events have proved that critical infrastructure are vulnerable to natural catastrophes, unintentional accidents and terrorist attacks. Protecting these systems is critical to avoid loss of life and to guard against economical upheaval. A systematic approach to plan security investments is paramount to guarantee that limited protection resources are utilized in the most effcient manner. This thesis provides a detailed review of the optimization models that have been introduced in the past to identify vulnerabilities and protection plans for critical infrastructure. The main objective of this thesis is to study new and more realistic models to protect transportation infrastructure such as railway and road systems against man made and natural disruptions. Solution algorithms are devised to effciently solve the complex formulations proposed. Finally, several illustrative case studies are analysed to demonstrate how solving these models can be used to support effcient protection decisions

Locating and Protecting Facilities Subject to Random Disruptions and Attacks

Recent events such as the 2011 Tohoku earthquake and tsunami in Japan have revealed the vulnerability of networks such as supply chains to disruptive events. In particular, it has become apparent that the failure of a few elements of an infrastructure system can cause a system-wide disruption. Thus, it is important to learn more about which elements of infrastructure systems are most critical and how to protect an infrastructure system from the effects of a disruption. This dissertation seeks to enhance the understanding of how to design and protect networked infrastructure systems from disruptions by developing new mathematical models and solution techniques and using them to help decision-makers by discovering new decision-making insights. Several gaps exist in the body of knowledge concerning how to design and protect networks that are subject to disruptions. First, there is a lack of insights on how to make equitable decisions related to designing networks subject to disruptions. This is important in public-sector decision-making where it is important to generate solutions that are equitable across multiple stakeholders. Second, there is a lack of models that integrate system design and system protection decisions. These models are needed so that we can understand the benefit of integrating design and protection decisions. Finally, most of the literature makes several key assumptions: 1) protection of infrastructure elements is perfect, 2) an element is either fully protected or fully unprotected, and 3) after a disruption facilities are either completely operational or completely failed. While these may be reasonable assumptions in some contexts, there may exist contexts in which these assumptions are limiting. There are several difficulties with filling these gaps in the literature. This dissertation describes the discovery of mathematical formulations needed to fill these gaps as well as the identification of appropriate solution strategies

Security of electric power systems : cascading outage analysis, interdiction model and resilience to natural disasters

textSecure electric power system operation is key to social warfare. However, recent years have seen numerous natural disasters and terrorist attacks that threat the grid security. This dissertation summarizes the efforts to develop a model to analyze cascading outages, an interdiction model to analyze worst-case attacks on power grids, and research on grid resilience to natural disasters. The developed cascading outage analysis model uses outage checkers to systematically simulate the system behavior after an initial disturbance, and calculate the potential cascading outage path and electric load shedding. The new interdiction model combines the previously developed medium-term attack-defense model with the short-term cascading outage analysis model to find worst-case terrorist attack. The dissertation also reviews the research on power grid resilience to natural disaster, and develops a framework to simulate the impacts of hurricanes.Electrical and Computer Engineerin

Multi-Level Multi-Objective Programming and Optimization for Integrated Air Defense System Disruption

The U.S. military\u27s ability to project military force is being challenged. This research develops and demonstrates the application of three respective sensor location, relocation, and network intrusion models to provide the mathematical basis for the strategic engagement of emerging technologically advanced, highly-mobile, Integrated Air Defense Systems. First, we propose a bilevel mathematical programming model for locating a heterogeneous set of sensors to maximize the minimum exposure of an intruder\u27s penetration path through a defended region. Next, we formulate a multi-objective, bilevel optimization model to relocate surviving sensors to maximize an intruder\u27s minimal expected exposure to traverse a defended border region, minimize the maximum sensor relocation time, and minimize the total number of sensors requiring relocation. Lastly, we present a trilevel, attacker-defender-attacker formulation for the heterogeneous sensor network intrusion problem to optimally incapacitate a subset of the defender\u27s sensors and degrade a subset of the defender\u27s network to ultimately determine the attacker\u27s optimal penetration path through a defended network

Synthesis, Interdiction, and Protection of Layered Networks

This research developed the foundation, theory, and framework for a set of analysis techniques to assist decision makers in analyzing questions regarding the synthesis, interdiction, and protection of infrastructure networks. This includes extension of traditional network interdiction to directly model nodal interdiction; new techniques to identify potential targets in social networks based on extensions of shortest path network interdiction; extension of traditional network interdiction to include layered network formulations; and develops models/techniques to design robust layered networks while considering trade-offs with cost. These approaches identify the maximum protection/disruption possible across layered networks with limited resources, find the most robust layered network design possible given the budget limitations while ensuring that the demands are met, include traditional social network analysis, and incorporate new techniques to model the interdiction of nodes and edges throughout the formulations. In addition, the importance and effects of multiple optimal solutions for these (and similar) models is investigated. All the models developed are demonstrated on notional examples and were tested on a range of sample problem sets

A Quantitative Research Study on Probability Risk Assessments in Critical Infrastructure and Homeland Security

This dissertation encompassed quantitative research on probabilistic risk assessment (PRA) elements in homeland security and the impact on critical infrastructure and key resources. There are 16 crucial infrastructure sectors in homeland security that represent assets, system networks, virtual and physical environments, roads and bridges, transportation, and air travel. The design included the Bayes theorem, a process used in PRAs when determining potential or probable events, causes, outcomes, and risks. The goal is to mitigate the effects of domestic terrorism and natural and man-made disasters, respond to events related to critical infrastructure that can impact the United States, and help protect and secure natural gas pipelines and electrical grid systems. This study provides data from current risk assessment trends in PRAs that can be applied and designed in elements of homeland security and the criminal justice system to help protect critical infrastructures. The dissertation will highlight the aspects of the U.S. Department of Homeland Security National Infrastructure Protection Plan (NIPP). In addition, this framework was employed to examine the criminal justice triangle, explore crime problems and emergency preparedness solutions to protect critical infrastructures, and analyze data relevant to risk assessment procedures for each critical infrastructure identified. Finally, the study addressed the drivers and gaps in research related to protecting and securing natural gas pipelines and electrical grid systems

Developing novel optimization and machine learning frameworks to improve and assess the safety of workplaces

This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model\u27s robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to public safety. The second chapter proposes an innovative mathematical model which captures several essential features (e.g., the capacity of the facility and individual choices, heterogeneity of individual behavioral and choice sets, restriction on choice sets depending on the location of the shooter and facility orientation, and many others) which are essential for appropriately characterizing and analyzing the response strategy for civilians under an AS exposed environment. We demonstrate the applicability of the proposed model by implementing the effectiveness of the RUN.HIDE.FIGHT.® (RHF) program in an academic environment. Given most of the past incidents took place in built environments (e.g., educational and commercial buildings), there is an urgent need to methodologically assess the safety of the buildings under an active shooter situation. Finally, the third chapter aims to bridge this knowledge gap by developing a learning technique that can be used to model the behavior of the shooter and the trapped civilians in an active shooter incident. Understanding how the civilians responded to different simulated environments, a number of actions could have been undertaken to bolster the safety measures of a given facility. Finally, this study provides a customized decision-making tool that adopts a tailored maximum entropy inverse reinforcement learning algorithm and utilizes safety measurement metrics, such as the percentage of civilians who can hide/exit in/from the system, to assess a workplace\u27s safety under an active shooter incident

Critical Infrastructure Protection Metrics and Tools Papers and Presentations

Contents: Dr. Hilda Blanco: Prioritizing Assets in Critical Infrastructure Systems; Christine Poptanich: Strategic Risk Analysis; Geoffrey S. French/Jin Kim: Threat-Based Approach to Risk Case Study: Strategic Homeland Infrastructure Risk Assessment (SHIRA); William L. McGill: Techniques for Adversary Threat Probability Assessment; Michael R. Powers: The Mathematics of Terrorism Risk Stefan Pickl: SOA Approach to the IT-based Protection of CIP; Richard John: Probabilistic Project Management for a Terrorist Planning a Dirty Bomb Attack on a Major US Port; LCDR Brady Downs: Maritime Security Risk Analysis Model (MSRAM); Chel Stromgren: Terrorism Risk Assessment and Management (TRAM); Steve Lieberman: Convergence of CIP and COOP in Banking and Finance; Harry Mayer: Assessing the Healthcare and Public Health Sector with Model Based Risk Analysis; Robert Powell: How Much and On What? Defending and Deterring Strategic Attackers; Ted G. Lewis: Why Do Networks Cascade

Efficient Algorithms for Solving Facility Problems with Disruptions

This study investigates facility location problems in the presence of facility disruptions. Two types of problems are investigated. Firstly, we study a facility location problem considering random disruptions. Secondly, we study a facility fortification problem considering disruptions caused by random failures and intelligent attacks.We first study a reliable facility location problem in which facilities are faced with the risk of random disruptions. In the literature, reliable facility location models and solution methods have been proposed under different assumptions of the disruption distribution. In most of these models, the disruption distribution is assumed to be completely known, that is, the disruptions are known to be uncorrelated or to follow a certain distribution. In practice, we may have only limited information about the distribution. In this work, we propose a robust reliable facility location model that considers the worst-case distribution with incomplete information. Because the model imposes fewer distributional assumptions, it includes several important reliable facility location problems as special cases. We propose an effective cutting plane algorithm based on the supermodularity of the problem. For the case in which the distribution is completely known, we develop a heuristic algorithm called multi-start tabu search to solve very large instances.In the second part of the work, we study an r-interdiction median problem with fortification that simultaneously considers two types of disruption risks: random disruptions that happen probabilistically and disruptions caused by intentional attacks. The problem is to determine the allocation of limited facility fortification resources to an existing network. The problem is modeled as a bi-level programming model that generalizes the r-interdiction median problem with probabilistic fortification. The lower level problem, that is, the interdiction problem, is a challenging high-degree non-linear model. In the literature, only the enumeration method is applied to solve a special case of the problem. By exploring the special structure property of the problem, we propose an exact cutting plane method for the problem. For the fortification problem, an effective logic based Benders decomposition algorithm is proposed

Annual Report FY13/14

https://digitalcommons.memphis.edu/govpubs-tn-safety-homeland-security-annual-report/1008/thumbnail.jp