Cascading Effects of Fuel Network Interdiction

This thesis develops the Fuel Interdiction and Resulting Cascading Effects (FI&RCE) model. The study details the development and experimental testing of a framework for assessing the interdiction of a refined petroleum production and distribution network. FI&RCE uses a maximum flow mathematical programming formulation that models the transit of fuels from points of importation and refinement through a polyduct distribution network for delivery across a range of end user locations. The automated model accommodates networks of varying size and complexity. FI&RCE allows for parameters and factor settings that enable robust experimentation through implementation in MATLAB 2014 and the commercial solver CPLEX (Version 12.5). Experimental design allows the investigation of interdiction or disruption on supply and network infrastructure locations in order to support the strategic analytical needs of the user. Given a target set, FI&RCE provides measured responses for the resulting fuel availability and a valuation of economic loss. The value of economic loss feeds a Leontief based input-output model that assesses the cascading effects in the studied economy by implementing a mathematical program that optimizes the remaining industrial outputs. FI&RCE demonstrates a framework to investigate the military and cascading effects of a fuel interdiction campaign plan using a realistic case study

Models, Theoretical Properties, and Solution Approaches for Stochastic Programming with Endogenous Uncertainty

In a typical optimization problem, uncertainty does not depend on the decisions being made in the optimization routine. But, in many application areas, decisions affect underlying uncertainty (endogenous uncertainty), either altering the probability distributions or the timing at which the uncertainty is resolved. Stochastic programming is a widely used method in optimization under uncertainty. Though plenty of research exists on stochastic programming where decisions affect the timing at which uncertainty is resolved, much less work has been done on stochastic programming where decisions alter probability distributions of uncertain parameters. Therefore, we propose methodologies for the latter category of optimization under endogenous uncertainty and demonstrate their benefits in some application areas. First, we develop a data-driven stochastic program (integrates a supervised machine learning algorithm to estimate probability distributions of uncertain parameters) for a wildfire risk reduction problem, where resource allocation decisions probabilistically affect uncertain human behavior. The nonconvex model is linearized using a reformulation approach. To solve a realistic-sized problem, we introduce a simulation program to efficiently compute the recourse objective value for a large number of scenarios. We present managerial insights derived from the results obtained based on Santa Fe National Forest data. Second, we develop a data-driven stochastic program with both endogenous and exogenous uncertainties with an application to combined infrastructure protection and network design problem. In the proposed model, some first-stage decision variables affect probability distributions, whereas others do not. We propose an exact reformulation for linearizing the nonconvex model and provide a theoretical justification of it. We designed an accelerated L-shaped decomposition algorithm to solve the linearized model. Results obtained using transportation networks created based on the southeastern U.S. provide several key insights for practitioners in using this proposed methodology. Finally, we study submodular optimization under endogenous uncertainty with an application to complex system reliability. Specifically, we prove that our stochastic program\u27s reliability maximization objective function is submodular under some probability distributions commonly used in reliability literature. Utilizing the submodularity, we implement a continuous approximation algorithm capable of solving large-scale problems. We conduct a case study demonstrating the computational efficiency of the algorithm and providing insights

Strategic gas storage coordination among EU member states during supply crises: an optimization approach

Given the strong presence of natural gas in the European Union (EU) energy mix (25%), this work focuses on natural gas strategic storage reserves as a first non-market based solidarity measure to increase energy security among EU Member States in response to natural gas supply “high-impact, low-probability” events (HILP). It presents a two-stage stochastic LP optimization gas transport model minimizing costs to study the short-term resilience of the network to supply shocks when using strategic storage in a coordinated fashion and including a policy perspective (i.e., EU Regulation 2017/1938) to evaluate the impact of HILP on the level of demand curtailment, survival time, and the natural gas supply mix among MS in the EU. The model is implemented to analyze three applications related to natural gas storage: (1) assess resilience and security in the EU gas system during a real case of shock in demand during an exceptionally cold weather; (2) test the role of coordination in case of short-term HILP events in the EU natural gas network; and (3) examine solidarity measures, such as strategic storage, among EU Regional Risk Groups during gas system disruptions due to HILP events. Results highlight the value of gas infrastructure diversification and the role of storage in the gas market and its inherent value in the system. In particular, the cost efficiency found in the coordinated use of strategic storage during a short-term emergency emphasizes the importance of storage-based solidarity in mitigating the effects of HILP supply disruptions and securing resources to the grid. They indicate that geographical proximity alone, without solidarity interventions, is insufficient to provide system resilience and that solidarity interventions enhance survival time for Regional Risk Groups in the EU and reduce liquefied natural gas (LNG) and system costs, offering an additional insight on the interplay between storage and LNG.Open Acces

Gas models and three difficult objectives

Competition, security of supply and sustainability are at the core of EU energy policy. The Commission argues that making the European gas market more competitive (completing the internal gas market) will be instrumental in the pursuit of these objectives. We examine the question through the eyes of existing models of the European gas market. Can model tell us anything on this problem? Do they confirm or infirm the analysis of the Commission appearing in fundamental documents such the Green Paper, the Sector Inquiry or the new legislation package? We argue that results of existing models contradict a fundamental finding (paragraph 77) of the Sector Inquiry. We further elaborate on the basis of the economic assumption underlying the models, that changing the assumptions implicitly contained in paragraph 77 cast doubts on a large part of the reasoning justifying the completion of the internal gas market. We also explain that models could help arriving at a better definition of the relevant market, which is so important in the reasoning of the Commission. Last we also find model results that question the effectiveness of ownership unbundling. As to security of supply, we explain that models can also contribute to assess the value of additional infrastructure in the context of security of supply, but this potential seems largely untapped. Last we note that sustainability has not yet penetrated models of gas markets. We conclude by suggesting other area of immediate concern, possibly of higher technical difficulty, that modellers could address in future research.

Analysis of Disruptions in the Gulf of Mexico Oil and Gas Industry Supply Chain and Related Economic Impacts

Catastrophic events are human and economic tragedies in collaboration. Oil spills have enormous impacts on the local economy of the area and for the local labor markets. The Deepwater Horizon oil spill was caused by an explosion on semisubmersible drilling rig (Macondo) on April 20, 2010. Another regional disaster, Hurricane Katrina as it ripped over the core of the Gulf of Mexico producing zone, one of the most important oil and gas production region. With Geological complexities, continued of drilling and production in GoM increases the risk of having leak/spill. Therefore, the Econometrics methods, and Modeling to forecast impacts of potential disasters are utilized and conduct optimization modeling to capture key components for building reasonable supply chain models of actual situations for petroleum industry in order to make the best possible choices consequences of disaster in this dissertation,. The dynamic response of a different of industrial sectors in Louisiana to oil and gas disasters is considered. The likely magnitude of the net economic impact of a major oil spill (Macondo) will be determined in terms of jobs and wages with Vector Autoregressive method. Forecast the potential impacts of future changes in employment after disaster on economy will be studied. In the second part, the offsetting economic injection due to BP expenditures in the economy, will estimate by economic impact analysis method, which is Input-output models. Then the gross economic damage, which is created by BP oil spill will be calculated. The final results provide beneficial knowledge on determining the potential economic impact of future large-scale catastrophes and helpful for companies to react better to the economic impact of events. At the end, a mathematical framework will be presented for optimal network design of oil and gas supply chain with application for Louisiana Offshore Oil Port (LOOP); due to determine the optimal oil flow through the mid-stream/ downstream networks and its profit even if it is experiencing natural/ man-made damages. The outcome of this work is a new distributed decision support framework which is intended to help optimize the profit for critical energy zone and to boost economy under unpredictable situations

RESILIENT AND STRUCTURALLY CONTROLLABLE DESIGN OF MULTI-LEVEL INFRASTRUCTURE NETWORKS UNDER DISRUPTIONS

An infrastructure network comprises of different entities that are connected by the flow of materials, products, information or electricity. Disruptions could occur at any section of the network for a wide variety of reasons. Some examples include: company mergers (e.g., Halliburton’s impending purchase of Baker Hughes), labor union strikes (e.g., labor strike on the west coast of the United States in 2002), sanctions imposed or lifted (e.g., economic sanctions against Iran being lifted by the UN in July 2015), plantations being destroyed (banana plantations were destroyed by Hurricane Mitch in 1998), air traffic being suspended due to weather or terrorism, main suppliers put out of commission by natural disasters (e.g., the 1999 earthquake in Taiwan disrupted semiconductor fabrication facilities), etc. A resilient infrastructure network is one that has the ability to recover quickly from disruptions and ensure customers are minimally affected, while the simultaneous design of operational and strategic decisions in all levels of the network structure are considered. It becomes very important to design a resilient multi-level infrastructure network in order to manage disruptions using appropriate pre-disruption and post-disruption restoration strategies. The capability of structural controllability can help in recovering a disrupted infrastructure network and increasing its resilience before, during and after the occurrence of disruptions. In this dissertation, the problem of applying structural controllability in order to design a resilient multi-level infrastructure network under disruptions with the selection of appropriate restoration strategies and consideration of the trade-off between effectiveness and redundancy in the resilience analysis is considered. The aforementioned problem has four aspects worth of consideration: a) multi-level network structures, b) restorations strategies, c) resilience analysis, and d) structural controllability. In this regard, the primary research question is defined as: What methods are required for designing a resilient infrastructure network under disruptions through selecting appropriate restoration strategies in a manner of applying structural controllability? The primary research question is broken into four secondary questions in respect to each four aspects of the considered problem as follows. - What is a method to design a multi-level infrastructure network (e.g., node-level and network-level structures) considering both operational and strategic decisions? - What is a method to design a resilient infrastructure network through selecting appropriate pre-disruption (e.g., facility fortification, backup inventory) and post-disruption (e.g., reconfiguration, flexible production and inventory capacity) restoration strategies? - What is a method to evaluate network resilience as a function of time considering effectiveness and redundancy measures (e.g., service level and transportation time as effectiveness measures and control cost as redundancy measure)? - What is a method to determine the minimum number of driver nodes (i.e., driver nodes or controllers are required for controlling networks) to get structurally controllable infrastructure networks? In response to the primary research question, two methods are proposed in this dissertation. The first method is the multi-level infrastructure network (MLIN) method which refers to the first aspect of the problem. The second method is the resilient and structurally controllable infrastructure network (RCIN) method which refers to the second, third and last aspects of the problem. Based on these two proposed methods, the main created new knowledge in this dissertation is in tailoring and incorporating the structural controllability theory in the resilience analysis of disrupted infrastructure networks. The proposed MLIN and RCIN methods are verified and validated using two examples from the energy industry in the context of the validation square. An example of a network of electric charging stations for plug-in hybrid electric vehicles using renewable energy and power grid as sources of energy is used to demonstrate and validate the MLIN method. An example of a network of a multi-product European petroleum industry is used to demonstrate and validate the RCIN method. Although the proposed methods are solved for the two examples, both of them are generalizable to be applicable to any network-based complex engineered systems under disruptions

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development