This paper presents a technology selection algorithm to quantify both tangible and intangible benefits in fuzzy environment. Specifically, it describes an application of the theory of fuzzy sets to hierarchical structural analysis and economic evaluations. From the analytical point of view, decision-makers are asked to express their opinions on comparative importance of various factors in linguistic terms rather than exact numerical values. These linguistic variable scales, such as 'very high', 'high', 'medium', 'low' and 'very low', are then converted into fuzzy numbers, since it becomes more meaningful to quantify a subjective measurement into a range rather than in an exact value. By aggregating the hierarchy, the preferential weight of each alternative technology is found, which is called fuzzy appropriate index. The fuzzy appropriate indices of different technologies are then ranked and preferential ranking orders of technologies are found. From the economic evaluation perspective, a fuzzy cash flow analysis is employed. Since conventional engineering economic analysis involves uncertainty about future cash flows where cash flows are defined as either crisp numbers or risky probability distributions, the results of analysis may obscure. To deal quantitatively with imprecision or uncertainty, cash flows are modeled as triangular fuzzy numbers which represent 'the most likely possible value', 'the most pessimistic value' and 'the most optimistic value'. By using this algorithm, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision-making.
ASJC Scopus subject areas
- Ceramics and Composites
- Computer Science Applications
- Metals and Alloys
- Industrial and Manufacturing Engineering