A Generalized Decision Support System for the Contracting Career Field

This research effort develops a generalized decision support system (DSS) to assist contracting career field managers in making recruiting and retention decisions. The DSS focuses on the skill level inventories of the contracting enlisted force. The interest in this research was identified by contracting career field managers due to the recent negative trends in recruitment and retention and the lack of analytical tools available. To accomplish this objective manpower models were developed using a combination of techniques gathered through interviews with Army and Air Force analysts and a literature review focusing on manpower modeling. The models developed in this study are intended to assist career field managers in recruiting and retaining the correct number and skill level mix of personnel in the contracting career field. The models are generalized enough to serve as a DSS for other Air Force Specialty Codes (AFSC) with minimal revision

Simulation-based analysis and optimization of the United States Army performance appraisal system.

In this dissertation, a discrete event simulation framework is considered to replicate the dynamics, structure, and regulatory constraints placed on the officers in the U.S. Army. Using performance appraisal data provided by the United States Army Human Resources Command, we create a multi-objective response function that quantifies the human behavior associated with evaluating subordinates. Utilizing simulation-optimization techniques for model validation enables estimating unknown input parameters, such as human behavior, based on historical data. Furthermore, the model allows users to analyze the effects of current constraints on the evaluation system and the effects of proposed personnel policy changes.The effectiveness of the performance appraisal system is based on its ability to accurately evaluate the officers\u27 performance levels. An initial analysis showed that 20.07\% of the officers in the system do not receive as many above average evaluations as their performance level warrants. Additionally, structural changes such as decreasing the average number of a rater\u27s subordinates from fifteen to five increases the number of misidentified personnel by 59.86\%. Ranking and selection statistical procedures assist in determining the optimal combination of input parameters such as forced distribution constraints placed on raters, frequency of moves, number of subordinates assigned to each rater, and rater behavior. The simulation will serve as a tool for policy analysis to recommend policies and behavior that maximizes the extent to which the performance appraisal system accurately identifies the most qualified employees. Consequently, the results demonstrate broad applicability of simulation-optimization in the field of manpower modeling and human resource management

Markov and Semi-markov Chains, Processes, Systems and Emerging Related Fields

This book covers a broad range of research results in the field of Markov and Semi-Markov chains, processes, systems and related emerging fields. The authors of the included research papers are well-known researchers in their field. The book presents the state-of-the-art and ideas for further research for theorists in the fields. Nonetheless, it also provides straightforwardly applicable results for diverse areas of practitioners

Development of an Optimal Replenishment Policy for Human Capital Inventory

A unique approach is developed for evaluating Human Capital (workforce) requirements. With this approach, new ways of measuring personnel availability are proposed and available to ensure that an organization remains ready to provide timely, relevant, and accurate products and services in support of its strategic objectives over its planning horizon. The development of this analysis and methodology was established as an alternative approach to existing studies for determining appropriate hiring and attrition rates and to maintain appropriate personnel levels of effectiveness to support existing and future missions. The contribution of this research is a prescribed method for the strategic analyst to incorporate a personnel and cost simulation model within the framework of Human Resources Human Capital forecasting which can be used to project personnel requirements and evaluate workforce sustainment, at least cost, through time. This will allow various personnel managers to evaluate multiple resource strategies, present and future, maintaining near “perfect” hiring and attrition policies to support its future Human Capital assets

Recruitment Capabilities and Attractive Compensation in Supporting of the Employee Retention Paradigm

Generally, employees who have high skills always accept offers from other companies or what we call "high caliber" or "potential" employees. These employees often go in and out of the company, which causes problems for every company. The purpose of this research is to determine the relationship between recruitment, compensation, and employee retention and to determine the most dominant indicators of the three variables mentioned above. The research uses a qualitative approach with a phenomenological approach. The sample is taken from 3 informants, where the average status is as a company leader. nvivo as a tool in analyzing the paradigm or employee retention model. There are four analyses carried out where, in the cluster analysis, it is found that there is a moderate relationship between recruitment, compensation, and employee retention. On the recruitment map for recruitment analysis, the most dominant indicators are competency-based recruitment and online recruitment. For compensation analysis, the most dominant indicators are salary and bonus. The last analysis map for employee retention shows the most dominant career path. It is these dominant indicators that are expected to help with the problem of employee retention in the company

Retention Prediction and Policy Optimization for United States Air Force Personnel Management

Effective personnel management policies in the United States Air Force (USAF) require methods to predict the number of personnel who will remain in the USAF as well as to replenish personnel with different skillsets over time as they depart. To improve retention predictions, we develop and test traditional random forest models and feedforward neural networks as well as partially autoregressive forms of both, outperforming the benchmark on a test dataset by 62.8% and 34.8% for the neural network and the partially autoregressive neural network, respectively. We formulate the workforce replenishment problem as a Markov decision process for active duty enlisted personnel, then extend this formulation to include the Air Force Reserve and Air National Guard. We develop and test an adaptation of the Concave Adaptive Value Estimation (CAVE) algorithm and a parameterized Deep Q-Network on the active duty problem instance with 7050 dimensions, finding that CAVE reduces costs from the benchmark policy by 29.76% and 17.38% for the two cost functions tested. We test CAVE across a range of hyperparameters for the larger intercomponent problem instance with 21,240 dimensions, reducing costs by 23.06% from the benchmark, then develop the Stochastic Use of Perturbations to Enhance Robustness of CAVE (SUPERCAVE) algorithm, reducing costs by another 0.67%. Resulting algorithms and methods are directly applicable to contemporary USAF personnel business practices and enable more accurate, less time-intensive, cogent, and data-informed policy targets for current processes

Simulation-Optimization, Markov Chain and Graph Coloring Approaches to Military Manpower Modeling and Deployment Sourcing

The Army manpower system is a integration of numerous elements that can be independently modeled. Identifying and closing gaps in modeling research can reduce workforce inefficiencies and costs. Military manpower models are predominantly focused on forecasting behavior and inventory within given demand requirements. Moreover, research directed towards predicting behavior is almost entirely disaggregated by pecuniary and non-pecuniary goals with disproportionate effort devoted to modeling the external factors that effect such behavior. This thesis proposes modeling approaches to improve the management capabilities of the Army\u27s manpower system. First, we consider a simulation-optimization approach to estimating workforce requirements examines the capabilities and limitations of Monte Carlo simulation and optimization methods within the context of workforce demand forecasting, modeling and planning. Specifically, we focus on these methods as a viable improvement for aligning strategic goals with workforce requirements. A general model is presented for estimating workforce requirements given uncertain demand. Using a real-world data example, we assess the benefits of this methodology to determine an optimal mix of workforce skills while providing the flexibility and robustness to incorporate uncertainty, assess risk and improve effectiveness of the workforce planning process. Second, we address the critical stay-or-leave decision associated with military retention. Personnel retention is one of the most significant challenges faced by the U.S. Army. Central to the problem is understanding the incentives of the stay-or-leave decision for military personnel. Using three years of data from the U.S. Department of Defense, we construct and estimate a Markov chain model of military personnel. Unlike traditional classification approaches, such as logistical regression models, the Markov chain model allows us to describe military personnel dynamics over time and answer a number of managerially relevant questions. Building on the Markov chain model, we construct a finite horizon stochastic dynamic programming model to study the monetary incentives of stay-or-leave decisions. The dynamic programming model computes the expected payoff of staying versus leaving at different stages of the career of military personnel, depending on employment opportunities in the civilian sector. We show that the stay-or-leave decisions from the dynamic programming model possess surprisingly strong predictive power, without requiring personal characteristics that are typically employed in classification approaches. Furthermore, the results of the dynamic programming model can be used as input in classification methods and lead to more accurate predictions. Overall, our work presents an interesting alternative to classification methods and paves the way for further investigations on personnel retention incentives. Finally, a graph coloring approach to deployment sourcing addresses one of the external factors of personnel inventory behavior, deployments. The configuration of persistent unit deployments has the ability to affect everything from individual perceptions of service palatability to operational effectiveness. There is little evidence to suggest any analytical underpinnings to U.S. Army deployment scheduling and unit assignment patterns. This paper shows that the deployment scheduling and unit assignment (DSUA) problem can be formulated as an interval graph such that modifications to traditional graph coloring algorithms provide an efficient mechanism for dealing with multiple objectives

Manpower planning in hierarchical organisations: a mixed integer programming approach

Manpower planning is concerned with planning the use of human resources. In this thesis, manpower planning is defined as the process of determining manpower policies which ensure that suitable numbers of qualified people are in appropriate positions at the right times in order to meet organisational goals, while taking account of the career development opportunities of the individuals within the organisation.A number of different mathematical models have been developed for manpower planning. These models are reviewed and it is noted that a weakness of the optimisation models which have been proposed is that promotion rates, i.e. the proportion of staff promoted per year, can vary substantially from year to year because of the limitations of the techniques used. Since staff morale is likely to be affected if promotion rates vary significantly from one year to another, the results from these models may be unacceptable to management. In this thesis a mixed integer programming (MIP) manpower planning model is developed for determining minimum cost manpower policies in which promotion rates remain stable over time, and which satisfy specified staffing level requirements. In this MIP model promotion rates are treated as decision variables by using a binary variable representation. An iterative procedure is developed for solving this MIP model.The computational aspects of using the MIP manpower planning model are investigated. A demonstration decision support system based on this MIP model is developed, and the use of this system is illustrated using representative data for a military manpower system. The experience with this demonstration system suggests that the approach could be developed to produce a practical tool to aid management decision making

Simulating and Optimizing: Military Manpower Modeling and Mountain Range Options

In this dissertation we employ two different optimization methodologies, dynamic programming and linear programming, and stochastic simulation. The first two essays are drawn from military manpower modeling and the last is an application in finance. First, we investigate two different models to explore the military manpower system. The first model describes the optimal retirement behavior for an Army officer from any point in their career. We address the optimal retirement policies for Army officers, incorporating the current retirement system, pay tables, and Army promotion opportunities. We find that the optimal policy for taste-neutral Lieutenant Colonels is to retire at 20 years. We demonstrate the value and importance of promotion signals regarding the promotion distribution to Colonel. Signaling an increased promotion opportunity from 50% to 75% for the most competitive officers switches their optimal policy at twenty years to continuing to serve and competing for promotion to Colonel. The second essay explores the attainability and sustainability of Army force profiles. We propose a new network structure that incorporates both rank and years in grade to combine cohort, rank, and specialty modeling without falling into the common pitfalls of small cell size and uncontrollable end effects. This is the first implementation of specialty modeling in a manpower model for U.S. Army officers. Previous specialty models of the U.S. Army manpower system have isolated accession planning for Second Lieutenants and the Career Field Designation process for Majors, but this is the first integration of rank and specialty modeling over the entire officer's career and development of an optimal force profile. The last application is drawn from financial engineering and explores several exotic derivatives that are collectively known Mountain Range options, employing Monte Carlo simulation to price these options and developing gradient estimates to study the sensitivities to underlying parameters, known as "the Greeks". We find that IPA and LR/SF methods are efficient methods of gradient estimation for Mountain Range products at a considerably reduced computation cost compared with the commonly used finite difference methods