An extended intelligent technique for multiple-attribute decision-making under single-valued neutrosophic sets and applications to sustainable development evaluation of rural ecotourism

Abstract

With the development of society and the times, the socialist modernization cause in China is constantly developing, and the improvement of the economy has also driven the development of rural ecotourism industry in China. The unique natural terrain and cultural customs in rural areas have promoted the continuous development of local ecotourism industry, which has had a positive impact on the economic development of rural areas and not only solved the employment problem of the majority of the population in rural areas. It can also bring a continuous stream of economic benefits and continuously improve the living standards of local residents. Rural ecotourism refers to a rural tourism activity that takes place in rural areas, supported by the agricultural industry, and attracted by the rural environment and typical rural ecotourism resources, with the concept of ecotourism. Rural ecotourism mainly takes the form of rural scenery, agricultural participation, and folk experience, integrating sightseeing experience, cognition, and tourism activities. The main principles of rural tourism development are agricultural support, people-oriented, ecological protection, community participation, income feedback, and ecological home construction. The sustainable development evaluation of rural ecotourism is a classical multiple-attribute decision-making (MADM) issue. In such paper, the generalized weighted Bonferroni mean (GWBM) operator and power average (PA) is constructed for MADM with single-valued neutrosophic sets (SVNSs). Then, the single-valued neutrosophic number power generalized BM (SVNNPGBM) operator is built and then the MADM decision methods are proposed based on the SVNNPGBM operator. Finally, an example for sustainable development evaluation of rural ecotourism and some comparative analysis were given to demonstrate the SVNNPGBM method.