Possibilistic Uncertainty Propagation and Compromise Programming in the Life Cycle Analysis of Alternative Motor Vehicle Fuels | Zendy

Raymond R. Tan | Zendy; Alvin B. Culaba | Zendy; M.R.I. Purvis | Zendy

Open Access

Possibilistic Uncertainty Propagation and Compromise Programming in the Life Cycle Analysis of Alternative Motor Vehicle Fuels

Author(s) -

Raymond R. Tan,

Alvin B. Culaba,

M.R.I. Purvis

Publication year - 2004

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2004.p0023

Subject(s) - computer science , automotive industry , identification (biology) , selection (genetic algorithm) , noise (video) , life cycle assessment , life cycle inventory , domain (mathematical analysis) , operations research , artificial intelligence , production (economics) , engineering , mathematical analysis , botany , macroeconomics , mathematics , economics , image (mathematics) , biology , aerospace engineering

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.

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