Possibilistic Uncertainty Propagation and Compromise Programming in the Life Cycle Analysis of Alternative Motor Vehicle Fuels
Raymond R. Tan*, Alvin B. Culaba**, and Michael R. I. Purvis***
*Chemical Engineering Department De La Salle University, Manila, 2401 Taft Avenue, 1004 Manila, Philippines
**Mechanical Engineering Department of De La Salle University
***Department of Mechanical & Design Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK
Data noise often does not allow definitive results to be drawn from life cycle assessments (LCAs). The use of possibility theory to model data uncertainty led to the development of an LCA model that is able to derive useful conclusions to a specified level of confidence. The specific decision domain in this study involves the identification and selection of the best environmental option from ten different automotive fuels.