Reasoning About the Reliability of Multi-version, Diverse Real-Time Systems

This paper is concerned with the development of reliable real-time systems for use in high integrity applications. It advocates the use of diverse replicated channels, but does not require the dependencies between the channels to be evaluated. Rather it develops and extends the approach of Little wood and Rush by (for general systems) by investigating a two channel system in which one channel, A, is produced to a high level of reliability (i.e. has a very low failure rate), while the other, B, employs various forms of static analysis to sustain an argument that it is perfect (i.e. it will never miss a deadline). The first channel is fully functional, the second contains a more restricted computational model and contains only the critical computations. Potential dependencies between the channels (and their verification) are evaluated in terms of aleatory and epistemic uncertainty. At the aleatory level the events ''A fails" and ''B is imperfect" are independent. Moreover, unlike the general case, independence at the epistemic level is also proposed for common forms of implementation and analysis for real-time systems and their temporal requirements (deadlines). As a result, a systematic approach is advocated that can be applied in a real engineering context to produce highly reliable real-time systems, and to support numerical claims about the level of reliability achieved

Stochastic Model for Power Grid Dynamics

We introduce a stochastic model that describes the quasi-static dynamics of an electric transmission network under perturbations introduced by random load fluctuations, random removing of system components from service, random repair times for the failed components, and random response times to implement optimal system corrections for removing line overloads in a damaged or stressed transmission network. We use a linear approximation to the network flow equations and apply linear programming techniques that optimize the dispatching of generators and loads in order to eliminate the network overloads associated with a damaged system. We also provide a simple model for the operator's response to various contingency events that is not always optimal due to either failure of the state estimation system or due to the incorrect subjective assessment of the severity associated with these events. This further allows us to use a game theoretic framework for casting the optimization of the operator's response into the choice of the optimal strategy which minimizes the operating cost. We use a simple strategy space which is the degree of tolerance to line overloads and which is an automatic control (optimization) parameter that can be adjusted to trade off automatic load shed without propagating cascades versus reduced load shed and an increased risk of propagating cascades. The tolerance parameter is chosen to describes a smooth transition from a risk averse to a risk taken strategy...Comment: framework for a system-level analysis of the power grid from the viewpoint of complex network

Air Force Institute of Technology Research Report 2019

This Research Report presents the FY19 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

The Police Response to Active Shooter Incidents

There have been many active shooter incidents in the United States since Columbine, and police agencies continue to modify their policies and training to reflect the lessons that are learned from each new tragedy. This report summarizes the state of the field as of 2014. The Police Executive Research Forum conducted research on these issues and held a one-day Summit in Washington, D.C., in which an overflow crowd of more than 225 police chiefs and other officials discussed the changes that have occurred, and where they are going from here

AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS

Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems

Electric System Vulnerabilities: Lessons from Recent Blackouts and the Role of ICT

In recent years, both Europe and America have experienced a significant number of major blackouts. This report specifically focuses on the events that affected Europe and North America during 2003, and provides a detailed analysis by critical comparison of diverse and authoritative information sources (UCTE, Eurelectric, national and international investigation committees, national authorities reports, etc).JRC.G.6-Sensors, radar technologies and cybersecurit

Preparedness Exercises 2.0: Alternative Approaches to Exercise Design That Could Make Them More Useful for Evaluating and Strengthening Preparedness

This article appeared in Homeland Security Affairs (May 2011), v.7Preparedness exercises play central roles in both the building and assessment of organizational readiness for future incidents. Though processes for designing and evaluating exercises are well established, there are opportunities to improve the value of exercises for strengthening preparedness and as tools for gathering assessment data. This article describe the application of systems analytical approach adapted from engineering that examines response operations as systems with potential failure modes that could hurt performance at future incidents. This methodology, which has been applied previously to preparedness measurement, is explored here as a tool for exercise design to focus it more tightly on key potential problem areas and to make it easier to use exercise data to explore preparedness for incidents that could differ considerably from the specific exercised scenario.Approved for public release; distribution is unlimited

Strategic Location and Dispatch Management of Assets in a Military Medical Evacuation Enterprise

This dissertation considers the importance of optimizing deployed military medical evacuation (MEDEVAC) systems and utilizes operations research techniques to develop models that allow military medical planners to analyze different strategies regarding the management of MEDEVAC assets in a deployed environment. For optimization models relating to selected subproblems of the MEDEVAC enterprise, the work herein leverages integer programming, multi-objective optimization, Markov decision processes, approximate dynamic programming, and machine learning, as appropriate, to identify relevant insights for aerial MEDEVAC operations

A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than $30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021

Developing Sense-and-Respond Capability in a Mobile Service Firm Enabled by Dispatching Technology: An Action Research Study

All organizations, including mobile services enterprises, must be able to adapt and respond to discontinuous and rapidly changing business environments. Although mobile service providers have considerable IT-enabled dispatching options, knowledge is limited on how to leverage these technologies to augment adaptive management practices that improve business performance and create customer benefits. Against this backdrop, my collaborative action research study adapted the framework and principles of sense-and-respond (S&R) adaptive enterprise design to help a mobile service provider, LSG, Inc., develop the transactional and transformational capabilities it needed to improve outcomes in providing field services for the State of Georgia’s lottery terminals. The dissertation examines how LSG leveraged its recent implementation of IT-enabled dispatching technology both to augment restructuring of its managerial framework and to develop adaptive strategies and modular capabilities that let it systematically sense and respond to rapid and unpredictable changes in its business environment. The study gave LSG an approach for developing and implementing adaptive enterprise design processes using the S&R framework as a heuristic to identify, modify, and redesign the command-and-control (C&C) organizational architecture and operational routines; this effort was augmented by new dispatching technology. My research revealed specific dynamic capabilities and guided senior managers’ implementation of new adaptive governance mechanisms, organizational learning processes, dynamic stakeholder resource commitments, and modular “customer-back” resource customization strategies. More generally, the research shows how adaptive enterprise design principles can transform and address the specific discontinuity challenges that small service enterprises face, and offers insights and understanding into how practitioner–researchers can use theory to leverage firm resources and assets to co-create operational value with stakeholders