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

Optimization models and system dynamics simulations to improve military manpower systems

textManpower policy decisions are an extension of traditional operations management problems. Manpower policies strive to place the appropriate and accurate numbers of the correct types of people in the right jobs at the necessary time. Managers create inventory by hiring new workers, either in entry level or more senior positions. Over time, managers promote workers to satisfy demand for more advanced positions. Managers face the challenge of determining the number of people to hire into entry level positions, the number of people already in the work force to promote to more senior level positions, and in some open systems when and how many experienced employees to hire into these senior positions. This dissertation studies and develops three different methods and approaches to provide improved decision support to a healthcare organization’s manpower system. Our research goal is to design models of the organization’s manpower system to improve human resource operations. The healthcare system of interest is the United States Army’s Medical Department (AMEDD). The research will be arranged in three sections. We explore current practices and build improved optimization manpower system models. We use multi objective decision analysis techniques to enhance the optimization models. Lastly, we construct a system dynamics simulation model of the manpower system to address the limitations in the optimization models. There are three main contributions of this dissertation to the operations management literature. First, the development of improved manpower optimization models can be extended to other manpower systems. Second, we develop a technique to assess the manpower system value based on a series of value scoring transformation functions and weighting the over two hundred sub objectives in the optimization manpower system’s objective function. This application of multiple objective decision analysis makes it possible to compare different manpower systems. The system dynamics simulation of a military manpower system is new to the operations management literature as is how we use the system dynamics simulation to update optimization model parameters to construct a more realistic manpower system model.Information, Risk, and Operations Management (IROM