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As we continue to model more complex systems, the validation of dynamical responses has come to the forefront of modeling and simulation. One form of dynamic response is when the output is a function of time. The proper evaluation of functional data over an array of desired input parameters is critical to achieving a robust validation assessment of a simulation model. We extend the correlation analysis (CORA) objective rating system to validate functional data across experimental regions. Functional regression analysis is used to generate surrogate estimations of the system response functions at points within the region where experimental observations are absent. These CORA scores provide a measure of disagreement at each desired parameter configuration. An overall score for model validity is achieved using a weighted linear combination of the individual CORA scores. Finally, an improved CORA size scoring metric is introduced.

Model validation is a vital step in the simulation development process to ensure that a model is truly representative of the system that it is meant to model. One aspect of model validation that deserves special attention is when validation is required for the transient phase of a process. The transient phase may be characterized as the dynamic portion of a signal that exhibits nonstationary behavior. A specific concern associated with validating a model's transient phase is that the experimental system data are often contaminated with noise, due to the short duration and sharp variations in the data, thus hiding the underlying signal which models seek to replicate. This paper proposes a validation process that uses wavelet thresholding as an effective method for denoising the system and model data signals to properly validate the transient phase of a model. This paper utilizes wavelet thresholded signals to calculate a validation metric that incorporates shape, phase, and magnitude error. The paper compares this validation approach to an approach that uses wavelet decompositions to denoise the data signals. Finally, a simulation study and empirical data from an automobile crash study illustrates the advantages of our wavelet thresholding validation approach.

Recent efforts to improve the statistical rigor of both developmental and operational testing include recommendations to consider second-order screening designs. In this paper, we provide a broad review of screening designs and second-order screening designs. We then consider a real-world wind tunnel experiment case study from the recent published literature and use subject matter expertise to consider the viability of second-order screening design, a recently developed design called the DSD+, to sufficiently screen important factors in complex, nonlinear systems of interest using just a single experimental design.

The use of Live, Virtual and Constructive (LVC) simulations are increasingly being examined for potential analytical use particularly in test and evaluation. In addition to system-focused tests, LVC simulations provide a mechanism for conducting joint mission testing and system of systems testing when fiscal and resource limitations prevent the accumulation of the necessary density and diversity of assets required for these complex and comprehensive tests. LVC simulations consist of a set of entities that interact with each other within a situated environment (i.e., world) each of which is represented by a mixture of computer-based models, real people and real physical assets. The physical assets often consist of geographically dispersed test assets which are interconnected by persistent networks and augmented by virtual and constructive entities to create the joint test environment under evaluation. LVC experiments are generally not statistically designed, but really should be. Experimental design methods are discussed followed by additional design considerations when planning experiments for LVC. Some useful experimental designs are proposed and a case study is presented to illustrate the benefits of using statistical experimental design methods for LVC experiments. The case study only covers the planning portion of experimental design. The results will be presented in a subsequent paper.

Live, virtual, and constructive (LVC) simulation technologies are well-established in the areas of technology demonstration, mission rehearsal, and exercises. A promising new role for LVC simulation technology is to facilitate weapon systems testing by producing defendable results. Requirements to test new defense systems within a system-of-systems context and within joint force scenarios have placed demands on physical test ranges that are not likely to be met. The LVC testing option promises the breadth and depth of defense systems, either as real or simulated assets, to potentially meet the new test demands. However, leveraging this technology to support testing will require a shift in the approaches used by the LVC community. In this paper, we discuss the challenges facing the LVC testing initiative, both from an experimental and from an architectural and implementation standpoint.

We present a case study that attempts to replicate the realism of a test range using a Live, Virtual and Constructive (LVC) simulation. Because resources are limited on a real test range, the Air Force Simulation and Analysis Facility at Wright-Patterson Air Force Base, Ohio, was tasked to build a simulation that emulated, to a high degree of fidelity, test range assets – the goal was to understand the impact of jamming on an enemy’s integrated air defense system. Before testing began, the simulated effects generated by the LVC simulation have to be viable, robust and realistic in order to provide credible data during later phases of experimentation. We present our approach to evaluating the correctness of the LVC simulation that considers sources of error associated with real-world radar measurements, errors caused by model abstraction and errors introduced due to real-time distributed simulation architectures, including shared state-space inconsistencies, coordinate conversion issues and a common time reference.

The history of military simulation dates back to the earliest ages of conflict. Throughout the history, military personnel has employed cognitive devices to comprehend and plan for conflict. This chapter provides a history of military simulation, starting with a review of the board-based war games , progressing through the computerization of those war games and completing with a discussion of the modern world of military simulation encompassing desktop-based analytical simulations, video games, and distributed training and analytical environments. It provides a background for the contributions of the military track that has been a pillar of the Winter Simulation Conference for many years.

The archives of mishap investigation reports provide a large database of documented system failures. We present a method that studies human error in these mishaps and quantitatively predicts areas of focus for new system development. Even very novel systems have similarities, either in structure or operation, to legacy systems. Knowing what design induced errors have plagued past human-machine interactions can guide new system requirements generation. This method is demonstrated through its application in a next generation military unmanned aerial system (UAS). For this, the last five years of US Air Force aircraft mishaps are studied using the Human Factors Analysis and Classification System (HFACS) and mapped to the domains of human systems integration (HSI).

This article discusses a novel agent-based modeling (ABM) approach to analyze the impact of warehouse congestion and presents results indicating the significant effect of congestion on cost and performance in various scenarios. In particular, the simulation represents the behaviors of the order pickers in a picker-to-part, low picking warehouse and focuses on representing the traffic and movements of the pickers. The key motivation for simulating this system is the lack of literature discussing models or simulations capable of representing the congestion component of order pickers, a component important in actual warehouse operations. The conceptual model of the simulation is described and justified using the Conceptual Model for Simulation Diagram™ and the simulation is constructed using the simulation software AnyLogic®. The simulation is operationally validated via a series of experiments performed to test the simulation’s results against the expected dynamics of the system as described in (Tompkins et al. 2003). After operationally validating the simulation, key results are discussed and it is shown that the ABM simulation paradigm is capable of quantitatively capturing new and traditionally difficult to explore dynamics in warehouse operations, including components of congestion not considered in literature.

Space-filling designs help experimenters to represent simulation outputs efficiently when entire input spaces cannot be exhaustively explored. Batch sequential designs allow for intermediate analyses to occur as later batches of experimental design points are being tested, given the ability to change later design points based on the outputs observed, and stop the experiment when the current observations are deemed sufficient to reduce experimental cost. Nearly orthogonal-and-balanced (NOAB) designs have good space-filling properties and can accommodate design spaces with continuous, discrete, and categorical factors. In this paper, mixed-integer linear programming (MILP) formulations used to find NOAB resolutions III, IV, and V designs are extended to construct batch sequential NOAB designs, where design stages can use different NOAB approaches. A case study is presented where a simultaneous construction approach results in overall more desirable designs than when using design augmentation, yet requires a predefined number of points for each design stage.

Metaheuristic search algorithms have become ubiquitous in the applied optimisation world. Various works have appeared classifying and improving these algorithms and the particular processes embedded within the algorithms. Successful metaheuristic approaches have a common general structure to their search processes. To this end, we offer a structural taxonomy of metaheuristic search methods. This taxonomy serves as a framework for constructing and evaluating metaheuristic approaches from a general structural perspective as well as for conducting empirical research regarding the effectiveness of more detailed structural components. Implementation mechanisms of the detailed components within each structural component are left for future taxonomy research and development.

Combat, unlike many real-world processes, tends to be singular in nature. This makes statistical analysis of the combat data problematic. Building stochastic simulation models of combat scenarios provides a means of studying in some detail the particulars of a combat scenario provided that the model accurately captures the scenario. The scenario of interest in this paper is the WWII Bay of Biscay U-boat campaign, and the model is an agent-based simulation. The challenge is how to validate, or ensure the adequacy of, the simulation representation of the combat scenario with the actual scenario on the basis of comparing simulation output to typically small-sample-size actual combat data. In this paper we give the details of a new statistical methodology for use in validating a mission-level, agent-based model of a historical combat scenario. We use the Bay of Biscay agent-based simulation and develop a bootstrapping technique applied to the small-sample-size actual data from the Bay of Biscay campaign. Results from this bootstrapping statistical technique are compared with commonly used statistical techniques and are shown to improve the comparison of agent-based simulation output to actual real-world data.

The systems engineering technical processes often lack the support of methods and tools that quantitatively integrate human considerations into early system design decisions. Because of this, engineers generally rely on qualitative judgments or delay critical decisions until late in the system lifecycle. Studies have revealed that this frequently results in cost, schedule, and effectiveness consequences. We begin this paper with a study of current issues in the application of systems engineering as a whole. We then examine how the challenges of human systems integration are a pervasive factor in those issues. Finally, we propose how to improve system engineering by better addressing the identified challenges.

The routing of unmanned ground vehicles for the surveillance and protection of key installations is modeled as a new variant of the Covering Tour Problem (CTP). The CTP structure provides both the routing and target sensing components of the installation protection problem. Our variant is called the in-transit Vigilant Covering Tour Problem (VCTP) and considers not only the vertex cover but also the additional edge coverage capability of the unmanned ground vehicle while sensing in-transit between vertices. The VCTP is formulated as a Traveling Salesman Problem (TSP) with a dual set covering structure involving vertices and edges. An empirical study compares the performance of the VCTP against the CTP on test problems modified from standard benchmark TSP problems to apply to the VCTP. The VCTP performed generally better with shorter tour lengths but at higher computational cost.

This paper discusses a prototype agent-based simulation for examining United States Air Force pilot retention issues. The United States Air Force expends significant time and resources training pil...

The Mobility Aircraft Availability Forecasting (MAAF) model prototype development and study effort was initiated to help the United States Air Force Air Mobility Command (AMC) answer the question, “How can we accurately predict mission capable (MC) rates?” While perfect prediction of aircraft MC rates is not possible, we investigate a simulation-based risk analysis approach. Current prediction methods utilize “after the fact” analyses and user opinion, making it difficult to perform quick, accurate, and effective analyses of potential limiting factors and policy changes, particularly in time-sensitive situations. This paper describes the MAAF proof-of-concept model and decision support system built to provide AMC managers the dynamic, predictive tools needed to better forecast aircraft availability. The simulation component featured new capabilities for mobility modeling to include dynamic definition of the configuration of a mobility system, dynamic definition of the capabilities of the individual airbases within a mobility system, improved representation of the aircraft objects within the model, and a new approach to modeling aircraft maintenance including the realistic consideration of partially mission capable aircraft. The development efforts and sample experimental results are recounted in this paper.

We consider vehicle routing problems in the context of the Air Force operational problem of routing unmanned aerial vehicles from base locations to various reconnaissance sites. The unmanned aerial vehicle routing problem requires consideration of heterogeneous vehicles, vehicle endurance limits, time windows, and time walls for some of the sites requiring coverage, site priorities, and asymmetric travel distances. We propose a general architecture for operational research problems, specified for vehicle routing problems, that encourages object-oriented programming and code reuse. We create an instance of this architecture for the unmanned aerial vehicle routing problem and describe the components of this architecture to include the general user interface created for the operational users of the system. We employ route building heuristics and tabu search in a symbiotic fashion to provide a user-defined level-of-effort solver interface. Empirical tests of solution algorithms parameterized for solution speed reveal reasonable solution quality is attained.

This paper presents research combining an agent-based modeling and simulation paradigm with game theory for an in silico historical analysis of the Bay of Biscay submarine war during WWII. The U-boat threat was of great concern to the Allies, prompting initial operational research efforts to devise counterstrategies. Focusing search efforts in the Bay of Biscay enabled an effective Allied response. Using the historical record as a means to create a reasonably accurate model of the U-boat campaign, we allow the resulting agents within the model to adapt their strategies to counter-opposition strategies. Model output data are examined with respect to the historical record and game theory. The results hold promise for extending the agent-based modeling paradigm into more complex military-based domains.

The Air Force's ability to deploy, employ, and sustain operations in forward operating locations is a key to mission success. An integral part of this new strategy involving forward operating locations is equipment prepositioning, to include: vehicles, aircraft support, consumable inventory, and munitions. Proper prepositioning strategies provide a means to deploy forces rapidly without resorting to an increased overseas presence. This research focuses on defining and developing a mathematical model to aid decision makers with a strategy for positioning and configuring prepositioned assets. This research places particular emphasis on the strategic, global prepositioning of the afloat prepositioning fleet (APF), the configuration of these ships with respect to precision guided weaponry, the development of a transportation plan in response to modeled contingencies, and a port selection and distribution strategy once the APF ship is tasked to support a contingency. In addition to the APF assets, the model considers U.S.-based supply points used to augment on-hand and APF-provided munitions assets. The primary objective is to minimize the overall response time involved with offloading these ships and transporting their cargo (the munitions) to the intended point of use. The Pre-Po model developed is a mixed integer program, implemented using the general algebraic modeling system (GAMS), solved using the XA solver package, and tested against a realistically stressing planning scenario. Analytical results and insights are presented along with avenues for further work.

A nagging limitation of teaching spreadsheet-based quantitative decision-making courses is the sometimes stilted view of simulation presented, a view that overly emphasizes sampling simulation at the expense of process simulation. Without a viable spreadsheet-based process simulation package, the best efforts aimed at overcoming this emphasis on sampling simulation have provided passing references to special purpose simulation packages focused on process simulation. We introduce and discuss SimQuick as an alternative approach for teaching process simulation within the context of a spreadsheet-based approach to teaching simulation. Although an early tool, and somewhat limited when compared to special purpose simulation packages, SimQuick provides a viable means for teaching the process of simulation modeling and reinforcing the salient features of process modeling as a quantitative technique.

For many years the US military has maintained a significant overseas presence. Changes in the international political landscape has led to military reductions, particularly in this overseas military presence. To meet increased deployment demands from US-based stations the US Air Force is restructuring to accommodate an Expeditionary Aerospace Force (EAF) concept. As the United States Air Force evolves into an EAF using tailored combat force packages to meet specific threats, the requisite support functions for the deployed forces will garner increasing attention. We propose and examine a simulation-based methodology for examining options to reduce the amount of support equipment deployed with a tailored force. The simulation model is based on aircraft system failures and the ensuing maintenance actions. These maintenance actions drive the utilisation of aerospace ground equipment used to support aircraft maintenance. Support equipment deployment levels are re-examined using expected equipment utilisation ...

We compare synthetic Generalized Assignment Problems (GAP) generated under two correlation-induction strategies: Implicit Correlation Induction (ICI) and Explicit Correlation Induction (ECI). We present computational results for two commercially-available solvers and four heuristics on 590 test problems. We conclude that the solvers' performances degrade as the population correlation between the objective function and capacity constraint coefficients decreases. However, the heuristics' performances improved as the absolute value of the same population correlation increases. We find that problems generated under ECI are more challenging than problems generated under ICI and may lead to a better understanding of the capabilities and limitations of the solution methods being evaluated. We recommend that one consider the purpose (s) of an experiment and types of solution procedures to be evaluated when determining what types of test problems to generate.

This paper presents the results of an empirical study of the effects of coefficient correlation structure and constraint slackness settings on the performance of solution procedures on synthetic two-dimensional knapsack problems (2KP). The population correlation structure among 2KP coefficients, the level of constraint slackness, and the type of correlation (product moment or rank) are varied in this study. Representative branch-and-bound and heuristic solution procedures are used to investigate the influence of these problem parameters on solution procedure performance. Population correlation structure, and in particular the interconstraint component of the correlation structure, is found to be a significant factor influencing the performance of both the algorithm and the heuristic. In addition, the interaction between constraint slackness and population correlation structure is found to influence solution procedure performance.

When a simulation operates over an array of input settings, it is critical that its validity across all settings is considered. This paper proposes an F-statistic, based on a ratio of mean square errors, to assess model validity. System data is fit to an Ordinary Least Squares response surface and the mean square errors between both system and model data and the fitted surface are calculated. The hypothesis under test is that system and model data that possess the same distributional properties will produce similar measures of disagreement with regard to the fitted surface. If the F-statistic indicates the mean square errors are statistically different, it is determined that the system and model data do not share distributional properties and the model is assessed as invalid. A notional example demonstrates the methodology and considerations to the method's confidence with respect to the model sample size is discussed.

The Science of Test (SOT) Research Consortium is sponsored and funded by both Office of Secretary of Defense (OSD) Director, Operational Test and Evaluation (DOT&E) and by the Test Resource Management Center (TRMC) within the OSD Director, Developmental Test and Evaluation. The consortium members include the Air Force Institute of Technology, Department of Operational Sciences, Arizona State University, School of Computing, Informatics, and Design Systems Engineering, Virginia Tech, Statistics Department and Naval Postgraduate School, Operations Research Department. The SOT research effort commenced in early CY 2011. This report summarizes the accomplishments through the second year of the research consortium. The consortium collectively contributes to the theory of the statistical rigor of test with contributions to the body of knowledge and to the practice of test with applied methods and consultations.

Safe exposure limits for directed energy sources are derived from a compilation of known injury thresholds taken primarily from animal models and simulation data. The summary statistics for these experiments are given as exposure levels representing a 50% probability of injury, or ED50, and associated variance. We examine biological variance in focal geometries and thermal properties and the influence each has in singlepulse ED50 threshold studies for 514-, 694-, and 1064-nanometer laser exposures in the thermal damage time domain. Damage threshold is defined to be the amount of energy required for a retinal burn on at least one retinal pigment epithelium (RPE) cell measuring approximately 10 microns in diameter. Better understanding of experimental variance will allow for more accurate safety buffers for exposure limits and improve directed energy research methodology.

Complex aircraft require periodic maintenance checks to assess needed repairs for continued vehicle availability. However, such checks are expensive and the associated aircraft downtime can reduce fleet mission effectiveness. The United States Air Force plans to consolidate the time-based (isochronal) C-5 aircraft major inspection activities for eight C-5 home stations into three locations. Isochronal inspections rely on a calendar method to schedule inspections and disregard actual flying hours between inspections. By having the same personnel perform these inspections for all flying units and by adopting commercial aircraft condition-based inspection strategies, the Air Force hopes to gain efficiencies in performing these inspections. Conversely, the site phase-out schedule and reduced number of inspection locations raises questions about whether overall C-5 mission capability will be reduced. These proposed revisions were simulated in a designed experiment to assess the impacts to fleet availability and inspection site workload.

capable of igniting res. Fire simulations supporting system-level survivability analyses depend on accurate characterization of these ash clouds. Researchers at the Air Force Institute of Technology are developing statistically-based empirical models that describe time-dependent ash cloud geometries given impact conditions. This research presents the operational modeling approach used to develop the deterministic boundary model representation of impact ashes and the overall approach coupling physics-based energy models with empirical-based boundary models to realize a simulation-based approach to the characterization of ballistic impact ashes. Potential use of these combined models in survivability simulations is also discussed.

The military establishment uses many types of models during the planning and execution of military operations. Optimization models have always been an important class of models. However, as military systems, the scenarios in which those military systems are employed, and the impact of operations involving those military systems become more complex, classic optimization techniques based on mathematical programming can become impractical. Advances in computing capabilities have helped advance the use of heuristics to quickly find reasonable solutions to complex optimization problems. This article provides an overview of the more popular and powerful modern heuristics, most of which have analogies to natural systems. We also provide a representative survey of how these heuristics have been applied to specific classes of military optimization applications. Keywords: military modeling; simulated annealing; genetic algorithm; tabu search; heuristics; search methods

Advances in technology, software algorithms, and operations research methods provide the opportunity for effectively coupling the human decision maker with optimization modelling algorithms in large-scale systems operating in dynamic and uncertain environments. In military applications, such as search and rescue/destroy missions or real-time route planning or re-planning provide time windows within which critical decisions need to be made. Using a specially constructed human-computer integrated routing application, an evaluation was conducted to compare the effects of interactive model-based solutions with respect to automated solutions generated by mathematical modelling algorithms in the context of unmanned aerial vehicle route planning. Results indicate that significantly more high priority targets were covered in the human integrated approach compared to the automated solution without any significant degradation with respect to all the other dependent measures including percentage of total targets covered, low priority targets covered, total targets covered in threat zone, high priority targets covered in threat zone, and low priority targets covered in threat zone.

Modeling and simulation (M&S) has tremendous application in private and governmental sectors. From simple to complex constructive simulations, through object-oriented and agent-based simulations, all the way to the large complex distributed simulation environments, M&S affords the opportunity to examine just about anything of interest to the decision maker. We are pleased to present this combined special issue that provides works comprising this full range of M&S applications. Distributed environments composed of live, virtual and constructive (LVC) simulations have been used by the training and experimentation communities for over 10 years. The use of LVC simulation environments is being increasingly examined for potential analytical use, particularly in test and evaluation. The use of LVC for test and evaluation can increase the density and diversity of represented assets, which improves the robustness of the test. The integration of both real and simulated entities, often represented by different levels of realism and fidelity into a common synthetic battlespace or world, presents unique and interesting research challenges for both the analyst attempting to leverage this capability and secondly by the simulation architect attempting to produce credible data from a potentially complex distributed real-time software system. The analyst is often challenged with practical experimental design issues including a limited set of runs (i.e. small sample sizes) due to, for example, limited availability of pilots and operators for the live and virtual assets (i.e. the human subjects) and potential issues with unwanted noise and/or variance being introduced by including too much realism. The simulation architect is challenged with fundamental data consistency issues that arise when the desire to execute in real-time conflicts with the desire to interconnect geographically separated simulations. Additional cross-cutting challenges arise when the desire to reuse existing models and simulations often designed for different purposes are interconnected within the same simulated environment: from the analyst’s perspective of ‘‘will this affect the environment?’’ to the simulation engineer’s perspective of ‘‘are the models interoperable?’’. The first article by Hodson and Hill examines issues associated with using LVC simulation for testing purposes. Largely used in experimental and demonstration purposes, the promise of LVC simulation for test and evaluation purposes does not come without challenges, which are discussed in this lead article. The second article, ‘‘Effectiveness testing of non-lethal weapons’’ by Mezzacappa, argues that LVC simulation provides a crucial component of any research program on non-lethal weapons. The assessment of non-lethal weapons is problematic: it is hard to put humans in harm’s way. This paper discusses the US Army approach to non-lethal weapon assessment and the incorporation of LVC simulation into that testing. The third article, ‘‘Relational oriented systems engineering framework for flight training’’ by Dickerson and Holden addresses complex systems engineering concerns required to realize an LVC simulation for flight training experimentation. The authors introduce a Relational Oriented Systems Engineering (ROSE) methodology for the integration of systems within a system to realize a requisite LVC environment for complex system problems.

Wind tunnel test campaigns involve a large number of experimental data points, can take a long time to accomplish, and can consume tremendous resources. Design of Experiments (DOE) is a systematic approach to experimental design and analysis that has the potential to improve the efficiency and effectiveness of wind tunnel testing. We employ a legacy wind tunnel test campaign and compare the resulting data from that campaign to data generated using smaller experimental design strategies. The comparison is accomplished using a Monte Carlo sampling methodology coupled with a statistical comparison of the estimated surfaces. Initial results suggest a tremendous opportunity to reduce wind tunnel test efforts without losing test information.

Models and simulations have been widely used as a means to predict the performance of systems. Agentbased modeling and agent distillations have recently found tremendous success particularly in analyzing ground force employment and doctrine. They have also seen wide use in the social sciences modeling a plethora of real-life scenarios. The use of these models always raises the question of whether the model is correctly encoded (verified) and accurately or faithfully represents the system of interest (validated). The topic of agent-based model verification and validation has received increased interest. This chapter traces the historical roots of agent-based modeling. This review examines the modern influences of systems thinking, cybernetics as well as chaos and complexity on the growth of agent-based modeling. The chapter then examines the philosophical foundations of simulation verification and validation. Simulation verification and validation can be viewed from two quite different perspectives: the simulation philosopher and the simulation practitioner. Personnel from either camp are typically unaware of the other camp’s view of simulation verification and validation. This chapter examines both camps while also providing a survey of the literature and efforts pertaining to the verification and validation of agent-based models. The chapter closes with insights pertaining to agent-based modeling, the verification and validation of agent-based models, and potential directions for future research.

With increased interest in the overall employment of pilotless vehicles functioning in the ground, air, and marine domains for both defense and commercial applications, the need for high-fidelity simulation models for testing and validating the operational concepts associated with these systems is very high. This chapter presents a model-based approach that we adopted for investigating the critical issues in the command and control of remotely operated vehicles (ROVs) through an interactive model-based architecture. The domain of ROVs is highly dynamic and complex in nature. Hence, a proper understanding of the simulation tools, underlying system algorithms, and user needs is critical to realize advanced simulation system concepts. Our resulting simulation architecture integrates proven design concepts such as the model-view-controller paradigm, distributed computing, Web-based simulations, cognitive model-based high-fidelity interfaces and object-based modeling methods.

Genetic algorithms and local search techniques are used to optimize antenna placement for minimum coupling. The methodology uses a cylinder approximation of an aircraft fuselage and problems are of two and sixteen variables. An aggregate fitness function is constructed and monopole coupling and radiation patterns are calculated using uniform theory of diffraction techniques. Monopole coupling is reduced by GA and simplex search techniques by exploiting nulls in the surface wave coupling patterns while maintaining the desired antenna pattern coverage.

The author summarizes research results of a research effort in decision support system (DSS) technology for ULCE (Unified Life Cycle Engineering). Among the topics addressed were ULCE architectural issues, design measurement and methods research, ULCE-related design technique survey papers, and investigations into group support system technology. The technical papers produced influenced concurrent engineering efforts. The end result of this summary effort is to encapsulate the four-year research effort into a set of generalized findings and recommendations. Since the ULCE research program was a precursor to the current initiative in concurrent engineering (CE), or integrated product development (IPD), this set of findings and recommendations are discussed particularly with respect to an effort called the Requirements Analysis Process In Design (RAPID) program. >

In February 1999, a team of researchers from the Air Force Institute of Technology, the Wright State University and the University of Dayton teamed with researchers within the Air Force Research Laboratory, Logistics Research Division, Logistics Readiness Branch to propose a research agenda entitled, "Models, Web-based Simulations, and Integrated Analysis Techniques for Improved Logistical Performance." This proposal was submitted in response to the initial Dayton Area Graduate Studies Institute request for proposals under the Joint Air Force Research Laboratory/DAGSI Basic Research Program. The particular project supported as HE15 within the Human Effectiveness Directorate and entitled Logistics Readiness. The proposal successfully competed and was selected for contract award. Between June 1999 and July 2001, the researchers from the collaborating institutes pursued a collective research agenda, one that was closely aligned with the missions of the AFRL Logistics Readiness Branch as well as other organizations within the United States Air Force. This final technical report summarizes the research efforts under the contract, AFIT-HE-99-10. This report includes descriptions of the accomplishments of the research as well as listings of the numerous papers produced, presentations made, and masters thesis completed directly supporting, and supported by this research effort.