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

Aggregating group MCDM problems using a fuzzy Delphi model for personnel performance appraisal

Personnel performance appraisal is a tool towards achieving organization goals. Its main focus is to increase the abilities, merits and growth of personnel. We looked at the personnel performance appraisal as an element of group decision making model in which personnel are evaluated from different points of view. A fuzzy Delphi method and linguistic terms represented by triangular fuzzy numbers were applied to bring out qualitative and quantitative attributes and assess attributes weights and relative importance of evaluation group's viewpoints. We developed MCDM models for group personnel performance appraisal. All the known MCDM methods have their own advantages and drawbacks and therefore yield different results based on their various techniques. As a consequence, we presented a model for aggregation of the results of MCDM models

Suppliers Evaluation and Selection: A Comprehensive Model to Minimize the Risk Associated with Quality and Delivery

This research focuses on one of the main steps in outsourcing, supplierevaluation and selection. The main contributions of this research were twofold.First, best practices in the supplier’s value stream were identified that directly orindirectly impact a supplier’s quality and delivery. Second, a comprehensivesupplier evaluation and selection model was developed based on the valuestream concept to minimize the risk associated with two very critical supplierselection factors, quality and on-time delivery.A survey was conducted to identify best practices. The outcome of the surveywas used to develop a computer based supplier evaluation model, which couldbe used in conjunction with other existing supplier selection factors, such as price and others, to select suppliers

Embracing uncertainty in multi-step inference

This dissertation focuses on embracing the uncertainty that is associated with multi-step inference. Typically, statistical analyses consist of multiple steps that build on each other and are executed sequentially. Common practice is that each consecutive step ignores the uncertainty of the preceding steps. Throughout this dissertation, it is shown that not embracing uncertainty leads to overconfidence and biased conclusions. Furthermore, I have demonstrated that this uncertainty can be accounted for by averaging across models or by performing the steps that involve uncertainty simultaneously in a single model. For example, instead of averaging the scores from repeated measurements and then analyzing the averages, it is better to directly analyze the unaggregated data. These situations occur with scores given to patients by different raters, as in Chapters 3 and 4, but also with repeated measures ANOVA, as illustrated in Chapters 9 and 10. The discussion suggests several ideas for making the adoption of methods that appropriately account for uncertainty more easily accessible and more standardized. Overall, my hope with this dissertation is that the practice of ignoring uncertainty by tying together several inferential steps becomes a relic of the past and that future studies embrace the uncertainty in the individual steps by adopting multi-model inference

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Fuzzy expert systems in civil engineering

Imperial Users onl

A survey of preference-based reinforcement learning methods

Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function. However, designing such a reward function of ten requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert\u27s preferences instead of a hand-designed numeric reward. PbRL has gained traction in recent years due to its ability to resolve the reward shaping problem, its ability to learn from non numeric rewards and the possibility to reduce the dependence on expert knowledge. We provide a unified framework for PbRL that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity. The design principles include the type of feedback that is assumed, the representation that is learned to capture the preferences, the optimization problem that has to be solved as well as how the exploration/exploitation problem is tackled. Furthermore, we point out shortcomings of current algorithms, propose open research questions and briefly survey practical tasks that have been solved using PbRL