A GA-based borrowing strategy for construction smes in Hong Kong

Small and Medium Enterprise (SME) contributes 98% of the economy in Hong Kong. However, the semi-structured nature of finance optimization problems is still unsolved in financial decisions, especially in the construction industry. The research presented in this paper illustrates an adaptive genetic algorithm (AGA) approach to the selection of funding schemes by a modified construction loan acquisition model that was developed by Lam et al. (1998). Multiple projects being undertaken by a medium-size construction firm in Hong Kong were used as a real case study to demonstrate the application of AGA to the borrowing decision problems. A monthly borrowing strategy was finally achieved. SME Loan Guarantee Scheme (SGS) that was launched by the HKSAR was first identified as the source of external financing. Selection of sources of funding can be made such that a more accurate, objective and reliable borrowing decision can be provided for the decision- maker to analyze the financial options.