Safety and Security Management with Unmanned Aerial Vehicle (UAV) in Oil and Gas Industry

AbstractWe describe a mathematical model for UAV aided security operations in the oil and gas industry. Operating UAVs can provide seamless awareness on possible emergency situations such as oil spills, shipping incidents, industrial accidents, acts of terrorism, and so on. The primary goal of this model is to generate an optimal UAV operational schedule to meet surveillance needs in the areas of interest in each time period. The performance of these UAVs depends on the risk assessment on spatio-and-temporal characteristics of threats, specifications of available UAVs, and decision makers’ critical information requirements. The models are designed to provide insights into issues associated with designing and operating UAVs for strengthened maritime and port security

Liquefied Natural Gas Ship Route Planning Model Considering Market Trend Change

We consider a new biannual liquefi ed natural gas (LNG) ship routing and scheduling problem and a stochastic extension under boil-off gas (BOG) uncertainty while serving geographically dispersed multiple customers using a fl eet of heterogeneous vessels. We are motivated not only by contract trend changes to shorter ones but also by technological advances in LNG vessel design. The mutual coincidence of both transitions enables developing a new LNG shipping strategy to keep up with emerging market trend. We fi rst propose a deterministic LNG scheduling model formulated as a multiple vehicle routing problem (VRP). The model is then extended to consider BOG using a two-stage stochastic modeling approach in which BOG is a random variable. Since the VRP is typically a combinatorial optimization problem, its stochastic extension is much harder to solve. In order to overcome this computational burden, a Monte Carlo sampling optimization is used to reduce the number of scenarios in the stochastic model while ensuring good quality of solutions. The solutions are evaluated using expected value of perfect information (EVPI) and value of stochastic solution (VSS). The result shows that our proposed model yields more stable solutions than the deterministic model. The study was made possible by the NPRP award [NPRP 4-1249-2-492] from the Qatar National Research Fund (a member of the Qatar Foundation)

Risk-based Optimization Models for Maritime Safety and Security

Considering that unprotected assets and infrastructures in the Maritime industry are vulnerable to attacks, we present models and methodologies for protecting these maritime resources from malicious or terrorist attacks. Using risk-based analysis, we use conditional probabilities to establish relationships between consequences, vulnerabilities and threat incidences of maritime events. In the first part of this dissertation, we address safety/security of maritime assets. We consider vessel routing and scheduling in LNG vessels as a hazardous cargo, and present a risk-based methodology in the choice of alternate vessel routes between a liquefaction terminal and receiving depot(s). While derivations are presented for the quantification of each constituent of the risk-based model, actual historical data of terrorist/piracy attacks made available by a national consortium on the study of terrorism are used in the analysis approach. With a multivehicle routing model, we test our methodology and present results using a practical test case involving delivery of LNG. In the second part of this dissertation, we address safety/security of maritime infrastructures and use underwater sonars for threat detection. Models and algorithms are developed for providing surveillance to maritime infrastructures such as ports, harbors, jetties, etc. The methodologies in these models include a quantitative risk analysis approach, a network fortification approach, a greedy-based heuristic approach, and a robust optimization approach. The network fortification approach considers the ability of an intending ‘attacker’ to possess information related to resource limitations and protection procedure of a ‘defender’. Consequently, the ‘attacker’ attempts to use this information to evade detection, thus compromising safety and security of maritime infrastructures. In developing greedy-based algorithms to solve large scale problems in our placement methodology, we exploit the principle of submodularity to propose efficient solution algorithms with some theoretical guarantees. Lastly, we developed a robust formulation for our placement methodology to address uncertainties related to some modeling parameters. To illustrate that the new sonar placement methodologies developed help to improve protection coverage plans for maritime infrastructures, we use practical case studies to provide safety and security to ports. In addition, we provide analytical and experimental results on each of these studies.Industrial Engineering, Department o