Riverine sustainment 2012

Student Integrated ProjectIncludes supplementary materialThis technical report analyzed the Navy's proposed Riverine Force (RF) structure and capabilities for 2012. The Riverine Sustainment 2012 Team (RST) examined the cost and performance of systems of systems which increased RF sustainment in logistically barren environments. RF sustainment was decomposed into its functional areas of supply, repair, and force protection. The functional and physical architectures were developed in parallel and were used to construct an operational architecture for the RF. The RST used mathematical, agent-based and queuing models to analyze various supply, repair and force protection system alternatives. Extraction of modeling data revealed several key insights. Waterborne heavy lift connectors such as the LCU-2000 are vital in the re-supply of the RF when it is operating up river in a non-permissive environment. Airborne heavy lift connectors such as the MV-22 were ineffective and dominated by the waterborne variants in the same environment. Increase in manpower and facilities did appreciable add to the operational availability of the RF. Mean supply response time was the biggest factor effecting operational availability and should be kept below 24 hours to maintain operational availability rates above 80%. Current mortar defenses proposed by the RF are insufficient.N

A stochastic mixed integer programming approach to wildfire management systems

Wildfires have become more destructive and are seriously threatening societies and our ecosystems throughout the world. Once a wildfire escapes from its initial suppression attack, it can easily develop into a destructive huge fire that can result in significant loss of lives and resources. Some human-caused wildfires may be prevented; however, most nature-caused wildfires cannot. Consequently, wildfire suppression and contain- ment becomes fundamentally important; but suppressing and containing wildfires is costly. Since the budget and resources for wildfire management are constrained in reality, it is imperative to make important decisions such that the total cost and damage associated with the wildfire is minimized while wildfire containment effectiveness is maximized. To achieve this objective, wildfire attack-bases should be optimally located such that any wildfire is suppressed within the effective attack range from some bases. In addition, the optimal fire-fighting resources should be deployed to the wildfire location such that it is efficiently suppressed from an economic perspective. The two main uncertain/stochastic factors in wildfire management problems are fire occurrence frequency and fire growth characteristics. In this thesis two models for wildfire management planning are proposed. The first model is a strategic model for the optimal location of wildfire-attack bases under uncertainty in fire occurrence. The second model is a tactical model for the optimal deployment of fire-fighting resources under uncertainty in fire growth. A stochastic mixed-integer programming approach is proposed in order to take into account the uncertainty in the problem data and to allow for robust wildfire management decisions under uncertainty. For computational results, the tactical decision model is numerically experimented by two different approaches to provide the more efficient method for solving the model

Air Force Institute of Technology Research Report 2013

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Districting Problems - New Geometrically Motivated Approaches

This thesis focuses on districting problems were the basic areas are represented by points or lines. In the context of points, it presents approaches that utilize the problem\u27s underlying geometrical information. For lines it introduces an algorithm combining features of geometric approaches, tabu search, and adaptive randomized neighborhood search that includes the routing distances explicitly. Moreover, this thesis summarizes, compares and enhances existing compactness measures

Poisson process bandits:Sequential models and algorithms for maximising the detection of point data

In numerous settings in areas as diverse as security, ecology, astronomy, and logistics, it is desirable to optimally deploy a limited resource to observe events, which may be modelled as point data arising according to a Non-homogeneous Poisson process. Increasingly, thanks to developments in mobile and adaptive technologies, it is possible to update a deployment of such resource and gather feedback on the quality of multiple actions. Such a capability presents the opportunity to learn, and with it a classic problem in operations research and machine learning - the explorationexploitation dilemma. To perform optimally, how should investigative choices which explore the value of poorly understood actions and optimising choices which choose actions known to be of a high value be balanced? Effective techniques exist to resolve this dilemma in simpler settings, but the Poisson process data brings new challenges. In this thesis, effective solution methods for the problem of sequentially deploying resource are developed, via a combination of efficient inference schemes, bespoke optimisation approaches, and advanced sequential decision-making strategies. Furthermore, extensive theoretical work provides strong guarantees on the performance of the proposed solution methods and an understanding of the challenges of this problem and more complex extensions. In particular, Upper Confidence Bound and Thompson Sampling (TS) approaches are derived for combinatorial and continuum-armed bandit versions of the problem, with accompanying analysis displaying that the regret of the approaches is of optimal order. A broader understanding of the performance of TS based on non-parametric models for smooth reward functions is developed, and new posterior contraction results for the Gaussian Cox Process, a popular Bayesian non-parametric model of point data, are derived. These results point to effective strategies for more challenging variants of the event detection problem, and more generally advance the understanding of bandit decision-making with complex data structures

Constrained Task Assignment and Scheduling on Networks of Arbitrary Topology.

This dissertation develops a framework to address centralized and distributed constrained task assignment and task scheduling problems. This framework is used to prove properties of these problems that can be exploited, develop effective solution algorithms, and to prove important properties such as correctness, completeness and optimality. The centralized task assignment and task scheduling problem treated here is expressed as a vehicle routing problem with the goal of optimizing mission time subject to mission constraints on task precedence and agent capability. The algorithm developed to solve this problem is able to coordinate vehicle (agent) timing for task completion. This class of problems is NP-hard and analytical guarantees on solution quality are often unavailable. This dissertation develops a technique for determining solution quality that can be used on a large class of problems and does not rely on traditional analytical guarantees. For distributed problems several agents must communicate to collectively solve a distributed task assignment and task scheduling problem. The distributed task assignment and task scheduling algorithms developed here allow for the optimization of constrained military missions in situations where the communication network may be incomplete and only locally known. Two problems are developed. The distributed task assignment problem incorporates communication constraints that must be satisfied; this is the Communication-Constrained Distributed Assignment Problem. A novel distributed assignment algorithm, the Stochastic Bidding Algorithm, solves this problem. The algorithm is correct, probabilistically complete, and has linear average-case time complexity. The distributed task scheduling problem addressed here is to minimize mission time subject to arbitrary predicate mission constraints; this is the Minimum-time Arbitrarily-constrained Distributed Scheduling Problem. The Optimal Distributed Non-sequential Backtracking Algorithm solves this problem. The algorithm is correct, complete, outputs time optimal schedules, and has low average-case time complexity. Separation of the task assignment and task scheduling problems is exploited here to ameliorate the effects of an incomplete communication network. The mission-modeling conditions that allow this and the benefits gained are discussed in detail. It is shown that the distributed task assignment and task scheduling algorithms developed here can operate concurrently and maintain their correctness, completeness, and optimality properties.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91527/1/jpjack_1.pd

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics