Taxonomy method for green competitiveness evaluation of equipment manufacturing enterprises under picture 2-tuple linguistic environment

Author:

Geng Xiaonan12,Liu Peng12

Affiliation:

1. North China University of Science and Technology, Tangshan, Hebei, China

2. University of Perpetual Help System Dalta, Las Piñas Campus, Philippines

Abstract

The fourth industrial revolution, represented by intelligence, networking and greening, is breeding and breaking through, and China is increasingly Focus on green development, public awareness of environmental protection, and small changes are able to fundamentally change our industrial organization. The equipment manufacturing industry is an industry to which our country attaches great importance, and equipment manufacturing enterprises, as microscopic subjects, want to contribute to the “13th Five-Year Plan”. Equipment manufacturing industry is a highly valued industry in China, equipment manufacturing enterprises as a microscopic subject, in order to the “Thirteenth Five-Year Plan” equipment manufacturing industry rose to inject a strong impetus, must make essential changes, embrace the green economy, green development, and enhance the green competitiveness of enterprises. The green competitiveness evaluation of equipment manufacturing enterprises is the classical multiple criterion group decision making (MCGDM). In this paper, depending on the Taxonomy method, Taxonomy method will be extended to picture 2-tuple linguistic sets (P2TLSs) to address some MCGDM issues. Some essential concepts of picture 2-tuple linguistic sets (P2TLSs) are briefly reviewed. In addition, integrating the Taxonomy method with P2TLSs, the Taxonomy method with P2TLNs is constructed and all calculating procedures are simply depicted. Eventually, an empirical application for green competitiveness evaluation of equipment manufacturing enterprises has been offered to demonstrate this novel method and some comparative analysis are also made to confirm the merits of developed method.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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