New Interval-Valued Intuitionistic Fuzzy Behavioral MADM Method and Its Application in the Selection of Photovoltaic Cells

• Published in: Energies (Basel) Vol. 9; no. 10; p. 835
• Main Author: Zhang, Xiaolu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2016
• Subjects: Alternative energy sources; Crisps; Decision making; Expected utility; Fuzzy; Fuzzy sets; Hierarchies; Hypotheses; interval-valued intuitionistic fuzzy information; Linear programming; multiple attribute decision making; Nonlinear programming; Optimization; Photovoltaic cells; Ratings; renewable energy; Renewable resources; Solar cells
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en9100835

As one of the emerging renewable resources, the use of photovoltaic cells has become a promise for offering clean and plentiful energy. The selection of a best photovoltaic cell for a promoter plays a significant role in aspect of maximizing income, minimizing costs and conferring high maturity and reliability, which is a typical multiple attribute decision making (MADM) problem. Although many prominent MADM techniques have been developed, most of them are usually to select the optimal alternative under the hypothesis that the decision maker or expert is completely rational and the decision data are represented by crisp values. However, in the selecting processes of photovoltaic cells the decision maker is usually bounded rational and the ratings of alternatives are usually imprecise and vague. To address these kinds of complex and common issues, in this paper we develop a new interval-valued intuitionistic fuzzy behavioral MADM method. We employ interval-valued intuitionistic fuzzy numbers (IVIFNs) to express the imprecise ratings of alternatives; and we construct LINMAP-based nonlinear programming models to identify the reference points under IVIFNs contexts, which avoid the subjective randomness of selecting the reference points. Finally we develop a prospect theory-based ranking method to identify the optimal alternative, which takes fully into account the decision maker's behavioral characteristics such as reference dependence, diminishing sensitivity and loss aversion in the decision making process.