Search

Purpose This study aims to compare linear programming and stable marriage approaches to the personnel assignment problem under conditions of uncertainty. Robust solutions should exhibit reduced variability of solutions in the presence of one or more additional constraints or problem perturbations added to some baseline problems. Design/methodology/approach Several variations of each approach are compared with respect to solution speed, solution quality as measured by officer-to-assignment preferences and solution robustness as measured by the number of assignment changes required after inducing a set of representative perturbations or constraints to an assignment instance. These side constraints represent the realistic assignment categorical priorities and limitations encountered by army assignment managers who solve this problem semiannually, and thus the synthetic instances considered herein emulate typical problem instances. Findings The results provide insight regarding the trade-offs between traditional optimization and heuristic-based solution approaches. Originality/value The results indicate the viability of using the stable marriage algorithm for talent management via the talent marketplace currently used by both the U.S. Army and U.S. Air Force for personnel assignments.

Functional box plots satisfy two needs; visualization of functional data, and the calculation of important box plot statistics. Data visualization illuminates key characteristics of functional sets missed by statistical tests and summary statistics. The calculation of box plot statistics for functional sets permits a novel comparison more suited to functional data. The functional box plot uses a depth method to visualize and rank smooth functional curves in terms of a mean, box, whiskers, and outliers. The functional box plot improves upon other classic functional data analysis tools such as functional principal components and discriminant analysis for outlier detection. This research adds wavelet analysis as a generating mechanism along with depth for functional box plots to visualize functional data and calculate relevant statistics. The wavelet analysis of variance box plot tool gives competitive error rates in Gaussian test cases with magnitude outliers, and outperforms the functional box plot, for Gaussian test cases with shape outliers. Further, we show wavelet analysis is well suited at approximating irregular and noisy functional data and show the enhanced capability of WANOVA box plots to classify shape outliers which follow a different pattern than other functional data for both simulated and real data instances.

The Weibull distribution has long been a popular choice for modeling lifetime data of various mechanical and biological phenomena when the associated hazard rate function is constant or monotone in...

As systems grow more complex, so does our propensity to use computers to emulate complex real-world systems. Often these real-world systems possess dynamic response behavior over the operational domain of input parameter configurations. This domain is referred to as the design space or experimental region. It is critical we ensure that computer models which emulate such dynamic behavior be validated over the full design space. This paper presents a dual-interval validation methodology. Confidence intervals and tolerance intervals are developed based on a system response surface function. Model samples are compared to each interval to develop a complete model validation conclusion. The methodology is described, its robustness to noise and model lack-of-fit examined, and then it is applied to a well-established engineering validation challenge problem.

The military establishment, mimicking the business world in many respects, is abuzz with the rapid growth in big data, data analytics, machine learning, and statistical learning using artificial intelligence as well as supervised and unsupervised learning. But the challenge is really understanding what all these concepts mean for the military and how do these concepts relate the fundamental statistical principles historically employed by the defense operations research analyst or statistician. This chapter defines the key terms in the area and discusses the key aspects of the modern data challenge: big data, data engineering, data science, data analytics, and data mining. The chapter then focuses on data analytics and the range of methods and concerns associated with data analytics, and a discussion of the some of the techniques within the data analytics realm. The chapter closes with recommendations and concerns for the future.

Opinion dynamics is the study of how opinions in a group of individuals change over time and opinion dynamics models attempt to mathematically formulate this process. This research lays the foundations for, and develops the meta-contrast influence field model, a novel opinion dynamics model based on self-categorization theory. It improves on the existing meta-contrast model by providing a properly scaled, continuous influence basis while replicating key results of the original model. This influence basis is modular in nature, allowing future research to include other competing psychological forces in the mathematical formulation of influence. This flexibility is achieved while drastically reducing computational complexity, making feasible larger models of more psychologically complex agents.

The chronic pilot shortage that has plagued the United States Air Force over the past three years poses a national-level problem that senior military members are working to overcome. Unfortunately, not all pilot candidates successfully complete the necessary training requirements to become fully qualified Air Force pilots, which wastes critical time and resources and only further exacerbates the pilot shortage problem. Therefore, it is important for the Air Force to carefully consider whom they select to attend pilot training. This research examines historical specialized undergraduate pilot training (SUPT) candidate data leveraging a variety of machine learning techniques to obtain insights on candidate success. Computational experimentation is performed to determine how selected machine learning techniques and their respective hyperparameters affect solution quality. Results reveal that the extremely randomized tree machine learning technique can achieve nearly 94% accuracy in predicting candidate success. Additional analysis indicates degree type and commissioning source are the most important features in determining candidate success. Ultimately, this research can inform the modification of future SUPT candidate selection criteria and other related Air Force personnel policies.

We propose a bilevel math programming model for locating a heterogeneous set of sensors to maximize the minimum exposure of an intruder’s penetration path through a defended region. Our formulation also allows a defender to specify minimum probabilities of coverage for a subset of the located sensors (e.g., the most valuable sensors) and for high-value asset locations in the defended region. We reformulate the bilevel program to a single-level optimization problem for which instances can be readily solved using a commercial solver. Given the locations of a defender’s sensors, we additionally formulate three alternative path identification models corresponding to conceptually-motivated intrusion-path metrics. We examine a test instance for the air defense of a border region against intrusion by an enemy aircraft; upon identifying the optimal, respective defender asset location and intruder routing solutions, we examine the intruder-optimal solutions corresponding to each of three alternative metric-specific paths, illustrating the relative impact of an intruder choosing an inappropriate metric. Sensitivity analyses are conducted to examine the effect of several model parameters on solution quality and required computational effort.

Considered thousands of officer personnel records form the United States Air Force.Models for retention over several dozen career fields.Retention models focused on a 20-year time frame.Developed methods to combine retention models.Devised new metric for managerial use that provides insights into career fields requiring special attention. The United States Air Force organizes its workforce around rank structure and work specialty codes (Air Force Specialty Codes (AFSCs)). Unlike civilian organizations, all active duty Air Force personnel start at the entry level ranks. The challenge is to develop and manage personnel to fill a variety of skill sets at a variety of ranks over a 2030year planning horizon. The Air Force uses sustainment lines to accomplish many of its manpower management goals. However, the current methodology for developing these sustainment lines is not statistically defensible based on the actual retention data and does not provide management a means to identify specialty codes of concern. Leveraging methods from reliability theory, we utilize survivability functions constructed from historical retention data to develop and demonstrate a statistically defensible methodology for creating the sustainment lines at the core of the Air Force personnel management system and provide a tool for managers to focus attention on potential workforce problem areas.

High-resolution computer models can simulate complex systems and processes in order to evaluate a solution quickly and inexpensively. Many simulation models produce dynamic functional output, such as a set of time-series data generated during a process. These computer models require verification and validation (V&V) to assess the correctness of these simulations. In particular, the model validation effort evaluates if the model is an appropriate representation of the real-world system that it is meant to simulate. However, when assessing a model capable of generating functional output, it is useful to learn more than simply whether the model is valid or invalid. Specifically, if the model is deemed invalid, then what aspects of the model are incorrect? Is it possible to identify over what range the model data are a poor representation of the system data? Current V&V methods cannot identify these ranges. This paper proposes a wavelet analysis of variance (WANOVA) bisection method that first assesses model validity and can also identify the interval(s) over which the model is biased. The technique is illustrated using several simulation studies. Ultimately, this new method supports and expands the efficacy of model validation efforts.

Modeling and Simulation (M&S) is an ever growing field that utilizes increasingly complex and expensive simulations often requiring lots of time to run, can be difficult to maintain and analyze, but whose use continues to provide useful insight into leadership decisions. In a time critical decision situation, running such models may not be a practical approach to provide leadership the insights they need when they need those insights. Bayesian Networks (BN) are a useful analytical tool that offer understandable probabilistic relationships between variables, efficient computation, and quick analytical results. A BN indexed by time is a Dynamic Bayesian Network (DBN). A drawback to their use as a meta-model is the number of runs needed to train either the BN or DBN. This research introduces and demonstrates the use of bootstrapping techniques to reduce the number of actual simulation runs necessary to sufficiently train DBNs used as simulation meta-models. DBNs trained with independent simulation runs are compared to DBNs trained with bootstrapped-based samples developed on various sized subsets of actual simulation runs, the DBNs are compared statistically, and empirical examples are used to demonstrate the methodology. A method extending the DBN meta-model to multiple dimensions is presented and demonstrated. A case study using an actual combat simulation model completes the presentation.

Model validation is that critical component in the simulation development process that ensures a model is truly representative of the system that it is meant to model. Although there are numerous validation techniques described in the literature, many of these techniques still require some amount of subjective analysis in order to assess validity. This is particularly true with dynamic simulation output. To reduce or eliminate this subjectivity, this paper proposes a validation process that uses wavelet analysis of variance (WANOVA) as an effective method to statistically accept or reject a model as valid. This WANOVA validation approach performs statistical inference in the time-frequency domain to take advantage of wavelet sparsity and decorrelation. This process uses a test statistic based on thresholded wavelet coefficients to test the null hypothesis that the set of system data and model data are statistically equivalent. The validation technique is illustrated using a simulation study and empirical data from an automobile crash study.

Purpose The purpose of this paper is to demonstrate an improved method for forecasting the US Army recruiting. Design/methodology/approach Time series methods, regression modeling, principle components and marketing research are included in this paper. Findings This paper found the unique ability of multiple statistical methods applied to a forecasting context to consider the effects of inputs that are controlled to some degree by a decision maker. Research limitations/implications This work will successfully inform the US Army recruiting leadership on how this improved methodology will improve their recruitment process. Practical implications Improved US Army analytical technique for forecasting recruiting goals.. Originality/value This work culls data from open sources, using a zip-code-based classification method to develop more comprehensive forecasting methods with which US Army recruiting leaders can better establish recruiting goals.

The assessment of aircraft survivability against explosive munitions is an expensive undertaking. Test articles for both aircraft and weapon are scarce due to their high costs, leading to a limited...