Crisp mathematical programming techniques have shortcomings when used for measuring and evaluating achievement of organizational goals in a decision-making environment. Generally, in most real-life situations optimal achievement of multiple goals is rarely possible. In such cases, a compromise achievement of goals that leads to a satisfycing solution rather than an optimal solution bears more relevance. The present research introduces a framework to measure and evaluate the goals of efficiency and effectiveness in a fuzzy environment and is developed using Goal Programming, Data Envelopment Analysis and Fuzzy Set Theory. The proposed framework is called Fuzzy GoDEA and uses surrogate constraints to represent the efficiency and effectiveness goals. DEA type constraints are used to model the efficiency goal. The effectiveness goal is represented by the aggregate efficient contribution of the individual decision-making units toward achievement of the global organizational targets. Concepts of fuzzy set theory and goal programming are used to model the imprecision in goal achievement and the relative importance between goals. Additionally, the concept of minimal operational viability is introduced, though not included in the application due to unavailability of suitable data. The Fuzzy GoDEA framework accommodates crisp input and output data but allows imprecise specification of the aspiration levels for the efficiency and effectiveness goals. The imprecision in goal achievement is allowed through the specification of an interval of acceptable achievement rather than a crisp value. A membership function is defined for each fuzzy constraint associated with the efficiency and effectiveness goals and represents the degree of achievement of that constraint. Further, the Fuzzy GoDEA framework is extended into several variations that (i) allow the assignment of relative importance to the goals of efficiency and effectiveness and (ii) model scenarios where one of the goals of efficiency and effectiveness is crisp and the other fuzzy. The applied component of the research involves the application of the Fuzzy GoDEA framework to a newspaper preprint insertion process (NPIP). Detailed analyses of the results are presented to describe the information available from the Fuzzy GoDEA methodology that can be used in conjunction with conventional DEA analysis to assess and improve the efficiency and effectiveness performance of the NPIP system. / Master of Science

An airport is an important piece of infrastructure in air transportation system. This project focuses on measuring and ranking the efficiency of airports in the United States using the basic DEA, Ranking DEA, Goal programming and DEA and TOPSIS. In general, airport authorities of relatively inefficient airports are trying to benchmark the operational strategies of efficient airports. This project focuses on evaluating hub airports in the United States. ATL, LAX, and MEM airports are relatively efficient among forty four hub airports in the United States based on the performances and airport facilities of the 2000 year when the results of all applied methods in this project, the basic DEA ranking, the Cross Efficiency ranking, the Andersen-Petersen ranking and TOPSIS ranking method, are compared. The implication of this project is that airport authorities in the United States would benchmark these three airports to maximize operation and management efficiency for their airports. In general, most of the airports are handling passengers and freight. Therefore, ATL and LAX would be the most efficient hub airports in the United States. The capacities of airport facilities and more appropriate input data like financial data should be considered in the follow up research. / Master of Science