نوع مقاله : علمی - پژوهشی
نویسندگان
1 دانشجوی دکتری، دانشگاه آزاد اسلامی، واحد تهران جنوب پردیس کیش، ایران
2 استادیار، دانشکده مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران
3 استادیار، دانشگاه آزاد اسلامی، واحد صفادشت، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The most important issue in developing countries is the lack of proper infrastructures to facilitate the welfare of society and deal with problems in a desirable way. Due to the nature of infrastructures, the implementation of such projects requires significant funding, which, especially in developing countries, governments do not have enough capacity, so we must pay close attention to attract the new methods to finance projects. Therefore, it is necessary to use methods or models to determine the appropriate financing method for each organization according to its risk endurable level. The present study intends to provide a framework for determining the level of risk of financing methods in order to select the appropriate method in each project. In the first phase of the proposed framework be developing the qualitative method called content analysis, coding of interviews with experts and applying the Fuzzy AHP method, 17 risk criteria in the form of 7 main criteria including financing costs, risk of timely provision of resources, repayment structure The risk of interest rate fluctuations, the risk of exchange rate fluctuations, the risk of non-timely realization of project revenues and the risk of macroeconomic variables were identified and weighted. In the second phase, a fuzzy expert system was presented to predict the level of risk of financing methods. The results of the research in identifying the criteria showed that the risk of non-realization of project revenues in a timely manner, the economy risks and financing costs had the highest priority in the risk of project financing methods. Also, in the part of fuzzy expert system, firstly, in the development of the model, a new method was used to limit the number of rules in the knowledge base, and secondly, according to the validation performed, the accuracy of the model was determined to be 88%.
کلیدواژه‌ها [English]
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