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Molecular fuzzy least squares optimization-based decision making with entropy expert weighting for transparent solar panel installation investments | ||
Iranian Journal of Fuzzy Systems | ||
دوره 22، شماره 3، مرداد و شهریور 2025، صفحه 55-86 اصل مقاله (4.34 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22111/ijfs.2025.9246 | ||
نویسندگان | ||
G. Kou1، 2؛ H. Din¸cer3، 4، 5؛ S. Y¨uksel* 3، 6، 5؛ C. Demir3؛ S. Eti7 | ||
1School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China | ||
2School of Digital Media Engineering and Humanities, Hunan University of Technology and Business, Changsha 410205, China | ||
3School of Business, Istanbul Medipol University, Istanbul, Turkey | ||
4Department of Economics and Management, Khazar University, Baku, Azerbaijan | ||
5Clinic of Economics, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan | ||
6DepartmenClinic of Economics, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijant | ||
7IMU Vocational School, Istanbul Medipol University, Istanbul, Turkey | ||
چکیده | ||
The performance of transparent solar panel projects is affected by both technological and environmental and economic factors. However, the most important items should be identified for the efficient use of limited resources and effective risk management. There are few studies in the literature determining these factors. Because of this situation, businesses cannot direct their resources correctly and cannot create an effective strategy. A molecular fuzzy decision-making system is created to determine the most successful alternative investment policies for these projects to satisfy this gap in the literature. Various techniques are integrated in this model to reach the most effective solutions, such as molecular fuzzy sets to handle uncertainties, q-learning algorithm to weight experts, least square optimization (LSO) to calculate criteria weights and multi-objective particle swarm optimization (MOPSO) to rank investment strategies. The main contribution of this study is proposing an innovative molecular fuzzy decision-making model that manages uncertainties more effectively to select the most appropriate investment strategies for transparent solar panel investments. Considering molecular fuzzy sets allows for more accurate modelling of uncertainties and subjective evaluations. The results show that the most critical investment criterion for transparent solar panel investments is the regional solar radiation level. | ||
کلیدواژهها | ||
Molecular fuzzy sets؛ least square optimization؛ q-learning؛ solar panels؛ energy economics | ||
مراجع | ||
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