GA-Fuzzy Financial Model For Optimization of A BOT Investment Decision

Abstract

Financial modeling for investments to build/operate/transfer (BOT)-type projects is essentially intricate. The complexity stems mainly from two folds: multi-party involvement and uncertainty. Promoters need a systematic means for objective evaluation of financial performance measures in order to examine whether a certain level of profit margin and an attractive financial proposal to clients, are possible. A clear research gap is perceived in simultaneous evaluation of profitability and bid-winning potential from the promoters' perspective. By using a combination of genetic algorithms and the fuzzy set theory, an intelligent algorithm, is developed for optimization of conflicting financial interests in deriving the right mix of three key decision variables: equity ratio, concession length, and base price. Fuzzy sets are used to explicitly incorporate uncertainty in estimating economic and financial parameters due to lack of available data. Genetic algorithms is used for solving corresponding fuzzy objective function coupled with multiple constraints. A case study from prevailing literature demonstrates the excellent capability of the developed model to produce optimal financial scenario under uncertainty.