Research On Concessionaire Evaluation Model Based On Hybrid Multi-attribute Group Decision Making Under Risk

As a new form of financing for public infrastructure projects, BOT is widely used in the world by using the private funds and advanced technology. A critical success factor of BOT is the ability to find the right concessionaire not only be able to complete the task on time, and can be of high quality. The key point of selecting a appropriate concessionaire is that you find a suitable evaluation method.Through this we can fully evaluate the concessionaires. From abroad, the evaluation of concessionaire is mainly from the quantitative indicators, such as quantifiable indicators concession prices, construction costs, and the lowest price and the lowest construction cost is probablely on successing in the selecting concessionaire. This evaluation methods fewly consider both the qualitative indicators and the quantitative indicators, and the risk information and the non-risk information. The methods can not fully evaluate their ability to cope with uncertainty and risk, and then it is likely to lead to choosing the wrong concessionaire, causing renegotiating with the concessionaire or even the failure of the project.Based on the curren evaluation of the concessionaire, the purpose of this paper is to summarize the evaluation criteria BOT projects at home and abroad, making it a standard to reflect the long-term risk of BOT projects and divide the evaluation criteria into quantitative risk infactors, quantitative non-risk infactors, qualitative risk infactors and qualitative non-risk infactors.Then we can evaluate the concessionaire from a comprehensive aspects inorder to evaluate objectively. In the paper, we use the precise number evaluate the quantitative non-risk infactors, use the intuitionistic fuzy number evaluate the quantitative risk infactors, use the intuitionistic fuzzy number or fuzzy language evaluate the qualitative risk infactors and qualitative non-risk infactors. In the paper, we can establish the mathematical model, incorporate the evaluation of the experts, transform different language into one language, calculate the entropy of the evaluation matrix in order to calculate the weights of experts and attributes, use the IFWA operator gather the information of the experts and the attributes, get the evaluation of the concessionaire, and by calculating its nearness to the ideal solution to get the best concessionaire. The evaluation model realizes four information gathering, including the assembling the evaluation of the qualitative and quantitative aggregation, the assembling evaluation of risk and non-risk type aggregation, the assembling evaluation evaluation of different language and a numbe of experts.