Modeling an Adaptive System with Complex Queuing Networks and Simulation

An adaptive system differs from a non-adaptive system in that an adaptive system uses a specific process to identify and implement adaptations to system parameters during run time in an effort to increase system performance.;In order to develop an adaptive system one of the most important aspects is the ability to accurately predict and manage system behavior. If an unexpected event has occurred, accurate prediction of system behavior is needed in order to determine whether or not the system is able to continually meet expectations and/or requirements. When considering any possible adaptations to the system, one must be able to accurately predict their consequences as well.;In a feedback loop of an adaptive system performance analysis and prediction performance analysis and prediction may lead to a large number of different states. Therefore, the method of analyzing the system must be fast. For complex systems, however, the most popular method for predicting performance is simulation. Simulations, depending on the size of the system, are known for being slow.;In this thesis we develop a fast method for prediction of performance of a complex system. We use this method to allocate resources to the system initially, and then make decisions for system adaptation during runtime. Finally we test various modifications to the system in order to measure the robustness of our method

Share the Sky: Concepts and Technologies That Will Shape Future Airspace Use

The airspace challenge for the United States is to protect national sovereignty and ensure the safety and security of those on the ground and in the air, while at the same time ensuring the efficiency of flight, reducing the costs involved, protecting the environment, and protecting the freedom of access to the airspace. Many visions of the future NAS hold a relatively near-term perspective, focusing on existing uses of the airspace and assuming that new uses will make up a small fraction of total use. In the longer term, the skies will be filled with diverse and amazing new air vehicles filling our societal needs. Anticipated new vehicles include autonomous air vehicles acting both independently and in coordinated groups, unpiloted cargo carriers, and large numbers of personal air vehicles and small-scale point-to-point transports. These vehicles will enable new capabilities that have the potential to increase societal mobility, transport freight at lower cost and with lower environmental impact, improve the study of the Earth s atmosphere and ecosystem, and increase societal safety and security by improving or drastically lowering the cost of critical services such as firefighting, emergency medical evacuation, search and rescue, border and neighborhood surveillance, and the inspection of our infrastructure. To ensure that uses of the airspace can continue to grow for the benefit of all, a new paradigm for operations is needed: equitably and safely sharing the airspace. This paper is an examination of such a vision, concentrating on the operations of all types of air vehicles and future uses of the National Airspace. Attributes of a long-term future airspace system are provided, emerging operations technologies are described, and initial steps in research and development are recommended

Robustness and evolutionary dynamic optimisation of airport security schedules

Reducing security lane operations whilst minimising passenger waiting times in unforseen circumstances is important for airports. Evolutionary methods can design optimised schedules but these tend to over-fit passenger arrival forecasts resulting in lengthy waiting times for unforeseen events. Dynamic re-optimisation can mitigate for this issue but security lane schedules are an example of a constrained problem due to the human element preventing major modifications. This paper postulates that for dynamic re-optimisation to be more effective in constrained circumstances consideration of schedule robustness is required. To reduce over-fitting a simple methodology for evolving more robust schedules is investigated. Random delays are introduced into forecasts of passenger arrivals to better reflect actuality and a range of these randomly perturbed forecasts are used to evaluate schedules. These steps reduced passenger waiting times for actual events for both static and dynamic policies with minimal increases in security operations

Aeronautical Mobile Airport Communications System (AeroMACS)

To help increase the capacity and efficiency of the nation s airports, a secure wideband wireless communications system is proposed for use on the airport surface. This paper provides an overview of the research and development process for the Aeronautical Mobile Airport Communications System (AeroMACS). AeroMACS is based on a specific commercial profile of the Institute of Electrical and Electronics Engineers (IEEE) 802.16 standard known as Wireless Worldwide Interoperability for Microwave Access or WiMAX (WiMax Forum). The paper includes background on the need for global interoperability in air/ground data communications, describes potential AeroMACS applications, addresses allocated frequency spectrum constraints, summarizes the international standardization process, and provides findings and recommendations from the world s first AeroMACS prototype implemented in Cleveland, Ohio, USA

Resource allocation optimization problems in the public sector

This dissertation consists of three distinct, although conceptually related, public sector topics: the Transportation Security Agency (TSA), U.S. Customs and Border Patrol (CBP), and the Georgia Trauma Care Network Commission (GTCNC). The topics are unified in their mathematical modeling and mixed-integer programming solution strategies. In Chapter 2, we discuss strategies for solving large-scale integer programs to include column generation and the known heuristic of particle swarm optimization (PSO). In order to solve problems with an exponential number of decision variables, we employ Dantzig-Wolfe decomposition to take advantage of the special subproblem structures encountered in resource allocation problems. In each of the resource allocation problems presented, we concentrate on selecting an optimal portfolio of improvement measures. In most cases, the number of potential portfolios of investment is too large to be expressed explicitly or stored on a computer. We use column generation to effectively solve these problems to optimality, but are hindered by the solution time and large CPU requirement. We explore utilizing multi-swarm particle swarm optimization to solve the decomposition heuristically. We also explore integrating multi-swarm PSO into the column generation framework to solve the pricing problem for entering columns of negative reduced cost. In Chapter 3, we present a TSA problem to allocate security measures across all federally funded airports nationwide. This project establishes a quantitative construct for enterprise risk assessment and optimal resource allocation to achieve the best aviation security. We first analyze and model the various aviation transportation risks and establish their interdependencies. The mixed-integer program determines how best to invest any additional security measures for the best overall risk protection and return on investment. Our analysis involves cascading and inter-dependency modeling of the multi-tier risk taxonomy and overlaying security measurements. The model selects optimal security measure allocations for each airport with the objectives to minimize the probability of false clears, maximize the probability of threat detection, and maximize the risk posture (ability to mitigate risks) in aviation security. The risk assessment and optimal resource allocation construct are generalizable and are applied to the CBP problem. In Chapter 4, we optimize security measure investments to achieve the most cost-effective deterrence and detection capabilities for the CBP. A large-scale resource allocation integer program was successfully modeled that rapidly returns good Pareto optimal results. The model incorporates the utility of each measure, the probability of success, along with multiple objectives. To the best of our knowledge, our work presents the first mathematical model that optimizes security strategies for the CBP and is the first to introduce a utility factor to emphasize deterrence and detection impact. The model accommodates different resources, constraints, and various types of objectives. In Chapter 5, we analyze the emergency trauma network problem first by simulation. The simulation offers a framework of resource allocation for trauma systems and possible ways to evaluate the impact of the investments on the overall performance of the trauma system. The simulation works as an effective proof of concept to demonstrate that improvements to patient well-being can be measured and that alternative solutions can be analyzed. We then explore three different formulations to model the Emergency Trauma Network as a mixed-integer programming model. The first model is a Multi-Region, Multi-Depot, Multi-Trip Vehicle Routing Problem with Time Windows. This is a known expansion of the vehicle routing problem that has been extended to model the Georgia trauma network. We then adapt an Ambulance Routing Problem (ARP) to the previously mentioned VRP. There are no known ARPs of this magnitude/extension of a VRP. One of the primary differences is many ARPs are constructed for disaster scenarios versus day-to-day emergency trauma operations. The new ARP also implements more constraints based on trauma level limitations for patients and hospitals. Lastly, the Resource Allocation ARP is constructed to reflect the investment decisions presented in the simulation.Ph.D

The evolutionary game of pressure (or interference), resistance and collaboration

In the past few years, volunteers have produced geographic information of different kinds, using a variety of different crowdsourcing platforms, within a broad range of contexts. However, there is still a lack of clarity about the specific types of tasks that volunteers can perform for deriving geographic information from remotely sensed imagery, and how the quality of the produced information can be assessed for particular task types. To fill this gap, we analyse the existing literature and propose a typology of tasks in geographic information crowdsourcing, which distinguishes between classification, digitisation and conflation tasks. We then present a case study related to the “Missing Maps” project aimed at crowdsourced classification to support humanitarian aid. We use our typology to distinguish between the different types of crowdsourced tasks in the project and choose classification tasks related to identifying roads and settlements for an evaluation of the crowdsourced classification. This evaluation shows that the volunteers achieved a satisfactory overall performance (accuracy: 89%; sensitivity: 73%; and precision: 89%). We also analyse different factors that could influence the performance, concluding that volunteers were more likely to incorrectly classify tasks with small objects. Furthermore, agreement among volunteers was shown to be a very good predictor of the reliability of crowdsourced classification: tasks with the highest agreement level were 41 times more probable to be correctly classified by volunteers. The results thus show that the crowdsourced classification of remotely sensed imagery is able to generate geographic information about human settlements with a high level of quality. This study also makes clear the different sophistication levels of tasks that can be performed by volunteers and reveals some factors that may have an impact on their performance. In this paper we extend the framework of evolutionary inspection game put forward recently by the author and coworkers to a large class of conflict interactions dealing with the pressure executed by the major player (or principal) on the large group of small players that can resist this pressure or collaborate with the major player. We prove rigorous results on the convergence of various Markov decision models of interacting small agents (including evolutionary growth), namely pairwise, in groups and by coalition formation, to a deterministic evolution on the distributions of the state spaces of small players paying main attention to situations with an infinite state-space of small players. We supply rather precise rates of convergence. The theoretical results of the paper are applied to the analysis of the processes of inspection, corruption, cyber-security, counter-terrorism, banks and firms merging, strategically enhanced preferential attachment and many other