Graduation Year


Document Type




Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Grisselle Centeno, Ph.D.


Personnel assignments, Resource allocation, Data envelopment analysis, Tobit regression, Capability assessments


The development of effective personnel assignment methodologies has been the focus of research to academicians and practitioners for many years. The common theory among researchers is that improvements to the effectiveness of personnel assignment decisions are directly associated with favorable outcomes to organizations. Today, companies continue to struggle to develop high quality products in a timely fashion. This elevates the necessity to further explore and improve the decision-making science of personnel assignments. The central goal of this research is to develop a novel framework for human resource assignments in skill-based environments. An extensive literature review resulted in the identification of the following three areas of the general personnel assignment problem as potential improvement opportunities: determining assignment criteria, properly evaluating personnel capabilities, and effectively assigning resources to tasks.

Thus, developing new approaches to improve each of these areas constitute the objectives of this dissertation work. The main contributions of this research are threefold. First, this research presents an effective two-stage methodology to determine assignment criteria based on data envelopment analysis (DEA) and Tobit regression. Second, this research develops a novel fuzzy expert system for resource capability assessments in skill-based scenarios. The expert system properly evaluates the capabilities of resources in particular skills as a function of imprecise relationships that may exist between different skills. Third, this research develops an assignment model based on the fuzzy goal programming (FGP) technique. The model defines capabilities of resources, tasks requirements, and other important parameters as imprecise/fuzzy variables.

The novelty of the research presented in this dissertation stems from the fact that it advances the science of personnel assignments by combining concepts from the fields of statistics, economics, artificial intelligence, and mathematical programming to develop a solution approach with an expected high practical value.