System complexity has continued to increase with the development and application of new technologies. This increased complexity has created great concerns among people about the potential impact of a system on its ecological environment when considering such as plants, wildlife and clean air. A complete awareness of the potential impact requires a thorough understanding of how a system interacts with its ecological environment, and the results are dependent on the expertise of the engineer who is responsible for the design of the system and the analyst who evaluates the system Due to the complexity of these interactions and the difficulty in measuring the appropriate cause-and-effect relationships, a system's impact on its ecological environment has not received due attention. The above complexity and difficulty have led to two deficiencies in the current research of the system's environmental impact. One is the insufficient evaluation of its qualitative attributes. The other is an unstructured evaluation process where the analyst has to rely on qualitative attributes as major inputs while his/her expertise could not be modeled. As a consequence, the current research and evaluation process is deficient because of biases and lack of clarity. This report seeks to instill the necessary clarity into the decision-making process by structuring the decision maker's subjective knowledge. It is concluded that subjective preferences can be quantified and evaluated through utility function assessment. Alternatives are ranked and a final choice is made based on their utility. The modeling process described herein is made a lot more efficient and economical because of the computer software that integrates the assessment mechanisms into a user-friendly operational environment. After the deficiencies in the current evaluation process are identified, possible solutions are explored. The effectiveness of the Analytic Hierarchy Process (AHP), Multi-attribute Value Theory (MA VT), and Multi-attribute Utility Theory (MAUT) are compared. MAUT is the preferred approach based on solution requirements.

Master of Science