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

DiversiTree: Computing Diverse Sets of Near-Optimal Solutions to Mixed-Integer Optimization Problems

While most methods for solving mixed-integer optimization problems seek a single optimal solution, finding a diverse set of near-optimal solutions can often be more useful. State of the art methods for generating diverse near-optimal solutions usually take a two-phase approach, first finding a set of near-optimal solutions and then finding a diverse subset. In contrast, we present a method of finding a set of diverse solutions by emphasizing diversity within the search for near-optimal solutions. Specifically, within a branch-and-bound framework, we investigate parameterized node selection rules that explicitly consider diversity. Our results indicate that our approach significantly increases diversity of the final solution set. When compared with existing methods for finding diverse near-optimal sets, our method runs with similar run-time as regular node selection methods and gives a diversity improvement of up to 140%. In contrast, popular node selection rules such as best-first search gives an improvement of no more than 40%. Further, we find that our method is most effective when diversity is emphasized more in node selection when deeper in the tree and when the solution set has grown large enough.Comment: 30 pages, 11 figures, submitted to INFORMS Journal on Computin

Vulnerability Assessment and Re-routing of Freight Trains Under Disruptions: A Coal Supply Chain Network Application

In this paper, we present a two-stage mixed integer programming (MIP) interdiction model in which an interdictor chooses a limited amount of elements to attack first on a given network, and then an operator dispatches trains through the residual network. Our MIP model explicitly incorporates discrete unit flows of trains on the rail network with time-variant capacities. A real coal rail transportation network is used in order to generate scenarios to provide tactical and operational level vulnerability assessment analysis including rerouting decisions, travel and delay costs analysis, and the frequency of interdictions of facilities for the dynamic rail system

Connected infrastructure location design under additive service utilities

An infrastructure system usually contains a number of inter-connected infrastructure links that connect users to services or products. Where to locate these infrastructure links is a challenging problem that largely determines the efficiency and quality of the network. This paper studies a new location design problem that aims to maximize the total weighted benefits between users and multiple services that are measured by the amount of connectivity between users and links in the network. This problem is investigated from both analytical and computational points of view. First, analytical properties of special cases of the problem are described. Next, two integer programming model formulations are presented for the general problem. We also test intuitive heuristics including greedy and interchange algorithms, and find that the interchange algorithm efficiently yields near-optimum solutions. Finally, a set of numerical examples demonstrate the proposed models and reveal interesting managerial insights. In particular, we found that a more distance-dependent utility measure and a higher concentration of users help achieve a better total utility. As the population becomes increasingly concentrated, the optimal link design evolves from a linear path to a cluster of links around the population center. As the budget level increases, the installed links gradually sprawl from the population center towards the periphery, and in the case of multiple population centers, they grow and eventually merge into one connected component

Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantification

This work serves as a proof of concept for an automated tool to assist in the evaluation of experimental neutron cross section data in the resolved resonance range. The resonance characterization problem is posed as a mixed integer nonlinear program (MINLP). Since the number of resonances present is unknown, the model must be able to be determine the number of parameters to properly characterize the cross section curve as well as calculate the appropriate values for those parameters. Due to the size of the problem and the nonconvex nature of the parameterization, the optimization formulation is too difficult to solve as whole. A novel method is developed to decompose the problem into smaller, solvable windows and then stitch them back together via parameter-cardinality and parameter-value agreement routines in order to achieve a global solution. A version of quantile regression is used to provide an uncertainty estimate on the suggested cross section that is appropriate with respect to the experimental data. The results demonstrate the model's ability to find the proper number of resonances, appropriate average values for the parameters, and an uncertainty estimation that is directly reflective of the experimental conditions. The use of synthetic data allows access to the solution, this is leveraged to build-up performance statistics and map the uncertainty driven by the experimental data to an uncertainty on the true cross section

Identifying and mitigating supply chain risks using fault tree optimization

Although supply chain risk management and supply chain reliability are topics that have been studied extensively, a gap exists for solutions that take a systems approach to quantitative risk mitigation decision making and especially in industries that present unique risks. In practice, supply chain risk mitigation decisions are made in silos and are reactionary. In this article, we address these gaps by representing a supply chain as a system using a fault tree based on the bill of materials of the product being sourced. Viewing the supply chain as a system provides the basis to develop an approach that considers all suppliers within the supply chain as a portfolio of potential risks to be managed. Next, we propose a set of mathematical models to proactively and quantitatively identify and mitigate at-risk suppliers using enterprise available data with consideration for a firm’s budgetary constraints. Two approaches are investigated and demonstrated on actual problems experienced in industry. The examples presented focus on Low-Volume High-Value (LVHV) supply chains that are characterized by long lead times and a limited number of capable suppliers, which make them especially susceptible to disruption events that may cause delays in delivered products and subsequently increase the financial risk exposure of the firm. Although LVHV supply chains are used to demonstrate the methodology, the approach is applicable to other types of supply chains as well. Results are presented as a Pareto frontier and demonstrate the practical application of the methodology.This is an Accepted Manuscript of an article published by Taylor & Francis as Sherwin, Michael D., Hugh R. Medal, Cameron A. MacKenzie, and Kennedy J. Brown. "Identifying and mitigating supply chain risks using fault tree optimization." IISE Transactions (2019). DOI: 10.1080/24725854.2019.1630865. Posted with permission.</p

Allocating Protection Resources to Facilities When the Effect of Protection is Uncertain

<div><p>We study a new facility protection problem in which one must allocate scarce protection resources to a set of facilities given that allocating resources to a facility only has a <i>probabilistic</i> effect on the facility’s post-disruption capacity. This study seeks to test three common assumptions made in the literature on modeling infrastructure systems subject to disruptions: 1) <i>perfect protection</i>, e.g., protecting an element makes it fail-proof, 2) <i>binary protection</i>, i.e., an element is either fully protected or unprotected, and 3) <i>binary state</i>, i.e., disrupted elements are fully operational or non-operational. We model this facility protection problem as a two-stage stochastic program with endogenous uncertainty. Because this stochastic program is non-convex we present a greedy algorithm and show that it has a worst-case performance of 0.63. However, empirical results indicate that the average performance is much better. In addition, experimental results indicate that the mean-value version of this model, in which parameters are set to their mean values, performs close to optimal. Results also indicate that the perfect and binary protection assumptions together significantly affect the performance of a model. On the other hand, the binary state assumption was found to have a smaller effect.</p></div

Botnet detection using graph-based feature clustering

Abstract Detecting botnets in a network is crucial because bots impact numerous areas such as cyber security, finance, health care, law enforcement, and more. Botnets are becoming more sophisticated and dangerous day-by-day, and most of the existing rule based and flow based detection methods may not be capable of detecting bot activities in an efficient and effective manner. Hence, designing a robust and fast botnet detection method is of high significance. In this study, we propose a novel botnet detection methodology based on topological features of nodes within a graph: in degree, out degree, in degree weight, out degree weight, clustering coefficient, node betweenness, and eigenvector centrality. A self-organizing map clustering method is applied to establish clusters of nodes in the network based on these features. Our method is capable of isolating bots in clusters of small sizes while containing the majority of normal nodes in the same big cluster. Thus, bots can be detected by searching a limited number of nodes. A filtering procedure is also developed to further enhance the algorithm efficiency by removing inactive nodes from consideration. The methodology is verified using the CTU-13 datasets, and benchmarked against a classification-based detection method. The results show that our proposed method can efficiently detect the bots despite their varying behaviors