A novel framework for comprehensive value evaluation of cultural tourism resources with 2-tuple linguistic neutrosophic numbers multiple attribute decision making

Author:

Hou Jundan1,Liu Qian2,Dong Qi3

Affiliation:

1. Resource Management Department, Tangshan Normal University, Tangshan, Hebei, China

2. History, Culture and Law Department, Tangshan Normal University, Tangshan, Hebei, China

3. Computer Science Department, Tangshan Normal University, Tangshan, Hebei, China

Abstract

In recent years, with the rapid growth of the public’s demand for cultural connotation and cultural taste of tourism products, promoting the rapid development of the integration of cultural tourism, the development of cultural tourism boom has been set off nationwide. Cultural tourism resources are the premise and foundation of cultural tourism development. With the rise of cultural tourism fever, the collation and excavation of the cultural connotation and cultural value of various types of cultural tourism resources around the world has entered a more in-depth stage, which undoubtedly promotes the industrial transformation and utilization of resources, but in terms of the evaluation of the value of resources, there are more qualitative evaluations and few quantitative evaluations, which is largely due to the current academic classification of cultural tourism resources is not uniform, so that the evaluation of resources This is largely due to the difficulty of establishing the index system in the current academic community. The comprehensive value evaluation of cultural tourism resources is looked as the multiple attribute decision making (MADM) issue. In this paper, we extended the dua Hamy mean (DHM) operator and power avergae (PA) operator to 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic power DHM (2TLNPDHM) operator. Finally, a decision example for comprehensive value evaluation of cultural tourism resources is employed to show the 2TLNPDHM operator.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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