Evaluación de la vulnerabilidad de sistemas eléctricos por medio de programación multinivel: una revisión bibliográfica

Vulnerability studies can identify critical elements in electric power systems in order to take protective measures against possible scenarios that may result in load shedding, which can be caused by natural events or deliberate attacks. This article is a literature review on the latter kind, i.e., the interdiction problem, which assumes there is a disruptive agent whose objective is to maximize the damage to the system, while the network operator acts as a defensive agent. The non-simultaneous interaction of these two agents creates a multilevel optimization problem, and the literature has reported several interdiction models and solution methods to address it. The main contribution of this paper is presenting the considerations that should be taken into account to analyze, model, and solve the interdiction problem, including the most common solution techniques, applied methodologies, and future studies. This literature review found that most research in this area is focused on the analysis of transmission systems considering linear approximations of the network, and a few interdiction studies use an AC model of the network or directly treat distribution networks from a multilevel standpoint. Future challenges in this field include modeling and incorporating new defense options for the network operator, such as distributed generation, demand response, and the topological reconfiguration of the system.f the system.Los estudios de vulnerabilidad pueden identificar elementos críticos en los sistemas de distribución de potencia eléctrica con el fin de tomar medidas de protección contra posibles escenarios que pueden resultar en desconexión de carga (también llamado deslastre de carga), que puede ser ocasionada por eventos naturales o ataques deliberados. Este artículo es una reseña bibliográfica sobre el segundo tipo de casos, es decir, los del problema de interdicción, en el que se asume la existencia de un agente disruptivo cuyo objetivo es maximizar los daños ocasionados al sistema mientras el operador de red actúa como agente de defensa del mismo. La interacción no simultánea de estos dos agentes crea un problema de optimización multinivel y en la bibliografía se reportan varios modelos de interdicción y soluciones para abordar el problema. La contribución principal de este artículo es la presentación de consideraciones que deben tomarse en cuenta para analizar, modelar y resolver el problema de la interdicción, incluyendo las soluciones, métodos y técnicas más comunes para solucionarlo, así como futuros estudios al respecto. Esta revisión encontró que la mayoría de la investigación en el tema se enfoca en el análisis de los sistemas de transmisión, considerando las aproximaciones lineales de la red; algunos estudios en interdicción usan un modelo AC de la red o tratan las redes de distribución directamente desde un enfoque multinivel. Algunos retos en este campo son el modelado y la inclusión de nuevas opciones de defensa para el operador de la red, como la generación distribuida, la respuesta a la demanda y la reconfiguración topológica del sistema.&nbsp

RUNTIME ANALYSIS OF BENDERS DECOMPOSITION AND DUAL ILP ALGORITHMS AS APPLIED TO COMMON NETWORK INTERDICTION PROBLEMS

Attacker-defender models help practitioners understand a network’s resistance to attack. An assailant interdicts a network, and the operator responds in such a way as to optimally utilize the degraded network. This thesis analyzes two network interdiction algorithms, Benders decomposition and a dual integer linear program approach, to compare their computational efficiency on the shortest path and maximum flow interdiction problems. We construct networks using two operationally meaningful structures: a grid structure designed to represent an urban transportation network, and a layered network designed to mimic a supply chain. We vary the size of the network and the attacker's budget and we record each algorithm’s runtime. Our results indicate that Benders decomposition performs best when solving the shortest path interdiction problem on a grid network, the dual integer linear program performs better for the maximum flow problem on both the grid and layered network, and the two approaches perform comparably when solving the shortest path interdiction problem on the layered network.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

STRATEGIC DECISION MAKING IN SUPPLY CHAINS UNDER RISK OF DISRUPTIONS

Ph.DDOCTOR OF PHILOSOPH

Optimization Approaches for Improving Mitigation and Response Operations in Disaster Management

Disasters are calamitous events that severely affect the life conditions of an entire community, being the disasters either nature-based (e.g., earthquake) or man-made (e.g., terroristic attack). Disaster-related issues are usually dealt with according to the Disaster Operations Management (DOM) framework, which is composed of four phases: mitigation and preparedness, which address pre-disaster issues, and response and recovery, which tackle problems arising after the occurrence of a disaster. The ultimate scope of this dissertation is to present novel optimization models and algorithms aimed at improving operations belonging to the mitigation and response phases of the DOM. On the mitigation side, this thesis focuses on the protection of Critical Information Infrastructures (CII), which are commonly deemed to include communication and information networks. The majority of all the other Critical Infrastructures (CI), such as electricity, fuel and water supply as well as transportation systems, are crucially dependent on CII. Therefore, problems associated with CII that disrupt the services they are able to provide (whether to a single end-user or to another CI) are of increasing interest. This dissertation reviews several issues emerging in the Critical Information Infrastructures Protection (CIIP), field such as: how to identify the most critical components of a communication network whose disruption would affect the overall system functioning; how to mitigate the consequences of such calamitous events through protection strategies; and how to design a system which is intrinsically able to hedge against disruptions. To this end, this thesis provides a description of the seminal optimization models that have been developed to address the aforementioned issues in the general field of Critical Infrastructures Protection (CIP). Models are grouped in three categories which address the aforementioned issues: survivability-oriented interdiction, resource allocation strategy, and survivable design models; existing models are reviewed and possible extensions are proposed. In fact, some models have already been developed for CII (i.e., survivability-interdiction and design models), while others have been adapted from the literature on other CI (i.e., resource allocation strategy models). The main gap emerging in the CII field is that CII protection has been quite overlooked which has led to review optimization models that have been developed for the protection of other CI. Hence, this dissertation contributes to the literature in the field by also providing a survey of the multi-level programs that have been developed for protecting supply chains, transportation systems (e.g., railway infrastructures), and utility networks (e.g., power and water supply systems), in order to adapt them for CII protection. Based on the review outcomes, this thesis proposes a novel linear bi-level program for CIIP to mitigate worst-case disruptions through protection investments entailing network design operations, namely the Critical Node Detection Problem with Fortification (CNDPF), which integrates network survivability assessment, resource allocation strategies and design operations. To the best of my knowledge, this is the first bi-level program developed for CIIP. The model is solved through a Super Valid Inequalities (SVI) decomposition approach and a Greedy Constructive and Local Search (GCLS) heuristic. Computational results are reported for real communication networks and for different levels of both disaster magnitude and protection resources. On the response side, this thesis identifies the current challenges in devising realistic and applicable optimization models in the shelter location and evacuation routing context and outlines a roadmap for future research in this topical area. A shelter is a facility where people belonging to a community hit by a disaster are provided with different kinds of services (e.g., medical assistance, food). The role of a shelter is fundamental for two categories of people: those who are unable to make arrangements to other safe places (e.g., family or friends are too far), and those who belong to special-needs populations (e.g., disabled, elderly). People move towards shelter sites, or alternative safe destinations, when they either face or are going to face perilous circumstances. The process of leaving their own houses to seek refuge in safe zones goes under the name of evacuation. Two main types of evacuation can be identified: self-evacuation (or car-based evacuation) where individuals move towards safe sites autonomously, without receiving any kind of assistance from the responder community, and supported evacuation where special-needs populations (e.g., disabled, elderly) require support from emergency services and public authorities to reach some shelter facilities. This dissertation aims at identifying the central issues that should be addressed in a comprehensive shelter location/evacuation routing model. This is achieved by a novel meta-analysis that entail: (1) analysing existing disaster management surveys, (2) reviewing optimization models tackling shelter location and evacuation routing operations, either separately or in an integrated manner, (3) performing a critical analysis of existing papers combining shelter location and evacuation routing, concurrently with the responses of their authors, and (4) comparing the findings of the analysis of the papers with the findings of the existing disaster management surveys. The thesis also provides a discussion on the emergent challenges of shelter location and evacuation routing in optimization such as the need for future optimization models to involve stakeholders, include evacuee as well as system behaviour, be application-oriented rather than theoretical or model-driven, and interdisciplinary and, eventually, outlines a roadmap for future research. Based on the identified challenges, this thesis presents a novel scenario-based mixed-integer program which integrates shelter location, self-evacuation and supported-evacuation decisions, namely the Scenario-Indexed Shelter Location and Evacuation Routing (SISLER) problem. To the best of my knowledges, this is the second model including shelter location, self-evacuation and supported-evacuation however, SISLER deals with them based on the provided meta-analysis. The model is solved through a Branch-and-Cut algorithm of an off-the-shelf software, enriched with valid inequalities adapted from the literature. Computational results are reported for both testbed instances and a realistic case study

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

Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks

Cyber-Physical Systems (CPS) are increasingly complex and frequently integrated into modern societies via critical infrastructure systems, products, and services. Consequently, there is a need for reliable functionality of these complex systems under various scenarios, from physical failures due to aging, through to cyber attacks. Indeed, the development of effective strategies to restore disrupted infrastructure systems continues to be a major challenge. Hitherto, there have been an increasing number of papers evaluating cyber-physical infrastructures, yet a comprehensive review focusing on mathematical modeling and different optimization methods is still lacking. Thus, this review paper appraises the literature on optimization techniques for CPS facing disruption, to synthesize key findings on the current methods in this domain. A total of 108 relevant research papers are reviewed following an extensive assessment of all major scientific databases. The main mathematical modeling practices and optimization methods are identified for both deterministic and stochastic formulations, categorizing them based on the solution approach (exact, heuristic, meta-heuristic), objective function, and network size. We also perform keyword clustering and bibliographic coupling analyses to summarize the current research trends. Future research needs in terms of the scalability of optimization algorithms are discussed. Overall, there is a need to shift towards more scalable optimization solution algorithms, empowered by data-driven methods and machine learning, to provide reliable decision-support systems for decision-makers and practitioners