Goal Programming and Monte Carlo Simulation Apply to BOT/BOO Financial Evaluation

• Main Authors: Jain-Ming Lee, 李健銘
• Other Authors: Yu-Lin Huang
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/66226662842441927025

碩士 === 國立交通大學 === 土木工程系 === 89 === The feasibility of a BOT financial plan involves simultaneous decision-making by three major project players, namely the concession company, the bank syndicate, and the host government. The concession company usually focuses on financial efficiency (e.g., minimizing financing costs), while the bank cares about the “bankability” of the project (e.g., satisfying certain financial covenants), and the government cares about the “affordability” of the services to be provided. How to develop a common platform for those players to work together in BOT financial modeling is an open issue. The feasibility of a BOT financial plan also involves in-depth risk analysis. In practice, sensitivity/scenario analyses have been used in most BOT projects, but they deal only with a very limited set of scenarios developed by the users, and they lack a systematical approach for the users to discover, understand and model the interdependence of key financial variables. This research is a preliminary attempt to incorporate goal programming and Monte Carlo simulation into BOT financial planning and evaluation. Goal programming allows BOT project players to choose their own financial goals and to find out what would be needed in their respective decision making to achieve the goals. Monte Carlo simulation provides a systematical approach to identify key financial variables their and inter-dependence. It also provides a realistic way to develop and analyze an almost unlimited amount of scenarios. Computer software such as Microsoft Excel and @Risk are chosen for the development and implementation of an integrated BOT financial model. The success of the model, we believe, would greatly improve the efficiency and effectiveness of BOT financial planning and evaluation.